This document describes a hybrid recommendation system used at LinkedIn that generates "virtual profiles" to address cold start problems. Virtual profiles augment item profiles by inheriting rich features from members who have shown interest in that item. The system extracts features from user profiles to generate primary profiles for items, and then generates virtual profiles for items by selecting top features from affiliated user profiles using mutual information. An experiment on LinkedIn's community recommendation problem found virtual profiles outperformed collaborative filtering and improved recommendations for new users.
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
The document proposes a system to bridge socially enhanced virtual communities for business collaborations. The system discovers relevant existing businesses and potential business alliances through a semi-automated approach using broker discovery. Brokers help connect separate groups within a professional virtual community and find new relevant groups to join. Metrics are used to support discovering and selecting brokers to improve interactions, relationships, and trust between members of the virtual communities.
A Survey on Trust Inference Network for Personalized Use from Online Data RatingIRJET Journal
This document discusses a proposed new trust model called the "Web of Credit" (WoC) model for inferring personalized trust measures from online rating data in social networks. The WoC model constructs a trust network by tracking the flow of "credit" assigned from one user to another based on their ratings. It combines the objectiveness of reputation-based models which use rating histories, with the individualism of "Web of Trust" models which allow personalized trust measures. The document also presents the Core-Trust algorithm for inferring trust in this WoC-based network by considering factors like credit, risk, bias, and impedance derived from rating data. Experiments on real datasets showed the WoC model can infer trust more accurately than
This capstone report analyzes how user-generated metadata can enhance findability in social software applications like content tagging and recommender systems. The report examines these systems' strengths and weaknesses for information classification, retrieval, and discovery. Based on an analysis of six system-factor combinations, the report finds that content tagging systems have stronger overall findability than recommender systems, particularly for information classification. However, recommender systems exhibit strengths for information discovery. The report provides examples of content tagging and recommender systems to illustrate different design approaches.
#SPSVancouver 2016 - The importance of metadataVincent Biret
This document discusses the importance of metadata. It defines metadata as data about data and explains that metadata can improve navigation, findability, discoverability, and user experience. It also allows companies to build governance strategies and save money. The document then provides examples of how SharePoint and tools like Delve use metadata to enhance search and discovery of content.
Enhanced Performance of Search Engine with Multitype Feature Co-Selection of ...IJASCSE
Information world meet many confronts nowadays and one such, is data retrieval from a multidimensional and heterogeneous data set. Han & et al carried out a trail for the mentioned challenge. A novel feature co-selection for web document clustering is proposed by them, which is called Multitype Features Co-selection for Clustering (MFCC). MFCC uses intermediate clustering results in one type of feature space to help the selection in other types of feature spaces. It reduces effectively of the noise introduced by “pseudoclass” and further improves clustering performance. This efficiency also can be used in data retrieval, by implementing the MFCC algorithm in ranking algorithm of Search Engine technique. The proposed work is to apply the MFCC algorithm in search engine architecture. Such that the information retrieves from the dataset is retrieved effectively and shows the relevant retrieval.
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.
The EigenRumor algorithm calculates contribution scores for participants and information objects in online communities. It considers information provision and evaluation as links between participants and objects. The algorithm calculates three mutually reinforcing scores: authority score for participants' information provision ability, hub score for their evaluation ability, and reputation score for objects. The reputation score of an object is influenced by the authority score of its provider and hub scores of evaluators. In turn, authority and hub scores are influenced by the reputation scores of objects participants provide or evaluate. Calculating the scores through this mutually reinforcing process allows the algorithm to identify high contributors.
Structural Balance Theory Based Recommendation for Social Service PortalYogeshIJTSRD
There is enormous data present in our world. Therefore in order to access the most accurate information is becoming more difficult and complicated. As a result many relevant information gets missed which leads to much duplication of work and effort. Due to the huge search results, the user will generally have difficulty in identifying the relevant ones. To solve this problem, a recommendation system is used. A recommendation system is nothing but a filtering information system, which is used to predict the relevance of retrieved information according to the user’s needs for some criteria. Hence, it can provide the user with the results that best fit their needs. The services provided through the web normally provide huge records about any requested item or service. A proper recommendation system is used to separate this information result. A recommendation system can be improved further if supported with a level of trust information. That is, recommendations are prioritized according to their level of trust. Recommending appropriate needs social service to the target volunteers will become the key to ensure continuous success of social service. Today, many social service systems does not adopt any recommendation techniques. They provide advertisement or highlights request for a small commission. G. Banupriya | M. Anand "Structural Balance Theory-Based Recommendation for Social Service Portal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41216.pdf Paper URL: https://www.ijtsrd.comengineering/software-engineering/41216/structural-balance-theorybased-recommendation-for-social-service-portal/g-banupriya
The document proposes a system to bridge socially enhanced virtual communities for business collaborations. The system discovers relevant existing businesses and potential business alliances through a semi-automated approach using broker discovery. Brokers help connect separate groups within a professional virtual community and find new relevant groups to join. Metrics are used to support discovering and selecting brokers to improve interactions, relationships, and trust between members of the virtual communities.
A Survey on Trust Inference Network for Personalized Use from Online Data RatingIRJET Journal
This document discusses a proposed new trust model called the "Web of Credit" (WoC) model for inferring personalized trust measures from online rating data in social networks. The WoC model constructs a trust network by tracking the flow of "credit" assigned from one user to another based on their ratings. It combines the objectiveness of reputation-based models which use rating histories, with the individualism of "Web of Trust" models which allow personalized trust measures. The document also presents the Core-Trust algorithm for inferring trust in this WoC-based network by considering factors like credit, risk, bias, and impedance derived from rating data. Experiments on real datasets showed the WoC model can infer trust more accurately than
This capstone report analyzes how user-generated metadata can enhance findability in social software applications like content tagging and recommender systems. The report examines these systems' strengths and weaknesses for information classification, retrieval, and discovery. Based on an analysis of six system-factor combinations, the report finds that content tagging systems have stronger overall findability than recommender systems, particularly for information classification. However, recommender systems exhibit strengths for information discovery. The report provides examples of content tagging and recommender systems to illustrate different design approaches.
#SPSVancouver 2016 - The importance of metadataVincent Biret
This document discusses the importance of metadata. It defines metadata as data about data and explains that metadata can improve navigation, findability, discoverability, and user experience. It also allows companies to build governance strategies and save money. The document then provides examples of how SharePoint and tools like Delve use metadata to enhance search and discovery of content.
Enhanced Performance of Search Engine with Multitype Feature Co-Selection of ...IJASCSE
Information world meet many confronts nowadays and one such, is data retrieval from a multidimensional and heterogeneous data set. Han & et al carried out a trail for the mentioned challenge. A novel feature co-selection for web document clustering is proposed by them, which is called Multitype Features Co-selection for Clustering (MFCC). MFCC uses intermediate clustering results in one type of feature space to help the selection in other types of feature spaces. It reduces effectively of the noise introduced by “pseudoclass” and further improves clustering performance. This efficiency also can be used in data retrieval, by implementing the MFCC algorithm in ranking algorithm of Search Engine technique. The proposed work is to apply the MFCC algorithm in search engine architecture. Such that the information retrieves from the dataset is retrieved effectively and shows the relevant retrieval.
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.
The EigenRumor algorithm calculates contribution scores for participants and information objects in online communities. It considers information provision and evaluation as links between participants and objects. The algorithm calculates three mutually reinforcing scores: authority score for participants' information provision ability, hub score for their evaluation ability, and reputation score for objects. The reputation score of an object is influenced by the authority score of its provider and hub scores of evaluators. In turn, authority and hub scores are influenced by the reputation scores of objects participants provide or evaluate. Calculating the scores through this mutually reinforcing process allows the algorithm to identify high contributors.
Aiim Webinar Helen Mitchell Unified Search Final 7 21 2010Helen Mitchell
The document discusses search technologies and strategies for providing a unified view of private and public information across an organization. It covers definitions of key search concepts, challenges of information overload, examples of enterprise search, federated search, vertical search and summarization tools, as well as best practices and technologies to consider for unified search.
This document summarizes research on social capital among Iraqis conducted in 2005. The researchers administered a 192-item survey in Basra, Iraq and the Netherlands to measure trust and perceptions of ethnic/religious threats within Iraqi social networks. The researchers found relationships and trust were related to patterns of social capital. Perceptions of outgroups seemed related to social capital resources. The researchers shared their findings widely via email and the internet, attracting up to 350 readers/contributors daily. This rapid dissemination allowed them to quickly share timely research with stakeholders. The researchers conclude internet-facilitated communication can increase the visibility and impact of research.
The document describes the Semantic Communication Engine Innsbruck (SCEI), a software suite that supports online communication, feedback collection, and impact measurement across multiple channels. It introduces key terms and defines the problem of managing content distribution across different online channels. The proposed solution features a semantic layer that abstracts domain concepts from specific channels, and a "weaving process" that aligns content with channels. The architecture separates the software into a content management system and a distribution component called dacodi, which uses adapters to interface with individual channels in a standardized way.
Everything Self-Service:Linked Data Applications with the Information WorkbenchPeter Haase
The document discusses an information workbench platform that enables self-service linked data applications. It addresses challenges in building linked data applications like data integration and quality. The platform allows for discovery and integration of internal and external data sources. It provides intelligent data access, analytics, and collaboration tools through a semantic wiki interface with customizable widgets. Example application areas discussed are knowledge management, digital libraries, and intelligent data center management.
Findability Primer by Information Architected - the IA Primer SeriesDan Keldsen
The document discusses the importance of findability in the digital age. It defines findability as "the art and science of making content findable" and distinguishes it from simple search functions. Findability utilizes various technologies and techniques to help users efficiently locate relevant information among large volumes of digital content. These include tagging, taxonomies, semantic search, and natural language processing. The document provides an overview of different findability component technologies and their applications.
This document discusses managed metadata and taxonomies in SharePoint 2010. It defines key terms like metadata, taxonomy, and folksonomy. It provides examples of how an organization's information architecture can evolve over time from simple document storage to a more complex taxonomy. Best practices are suggested for designing taxonomies, including considerations for dynamic external tags, security, open vs. closed term sets, and content type hubs. Programming the managed metadata service in SharePoint 2010 is also briefly covered.
Rep on the Roll A peer to peer reputation system based on a rolling blockchainRichard Dennis
This document summarizes a paper that proposes a new blockchain-based reputation system called "Rep on the Roll" to address limitations of existing systems. It discusses how current centralized reputation systems used by e-commerce sites are not suitable for decentralized peer-to-peer networks. Existing P2P reputation systems have issues like optional participation and lack of identity management. The proposed system aims to solve problems like sybil attacks and improve scalability by reducing blockchain size by 92%, addressing a key challenge for all blockchain networks.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
This document discusses how metadata can be used to protect and derive value from content stored in public and private clouds. It proposes the concept of "Guardian Angels" that collect individualized metadata about a user's interactions with content. An "Invisible College" would allow anonymous aggregation of metadata from Guardian Angels to determine emergent meanings while preserving privacy. Standards are needed to incorporate these concepts and allow organic growth of associative metadata to enhance cloud services and information assets.
This document discusses how taxonomies and ontologies can improve enterprise search capabilities. It provides examples from case studies of organizations in the military, retail, and financial sectors. The case studies demonstrate how developing taxonomies, ontologies, content types and metadata structures helped organizations better classify, search and retrieve unstructured content to meet business needs.
Information Architecture Primer - Integrating search,tagging, taxonomy and us...Dan Keldsen
This document discusses the importance of taxonomy and classification within an information architecture. It defines key terms like taxonomy, thesaurus, ontology, and classification. It explains that taxonomy and classification help address the eternal problems of effectively cataloging and retrieving unstructured information. The document also discusses challenges like ambiguity, multiple meanings of words, and the importance of browsing versus searching in navigating large amounts of information.
The Web Information System of the National Institute for Astrophysics: differ...inscit2006
Caterina Boccato and Serena Pastore
National Institute for Astrophysics, Astronomical Observatory of Padova, Vicolo Osservatorio 5, 35122, Padova, ITALY
Towards enhanced user interaction to qualify web resources for higher-layered...Monika Steinberg
The document discusses enhancing user interaction to qualify web resources for knowledge transfer applications. It proposes a three-level model for assessing resource quality: first through metadata analysis, second through user interaction such as questionnaires, and third through intelligent analysis. It suggests that game-based interaction could motivate ongoing user participation in rating and ranking resources. An example uses a tag-based game on Flickr to increase image relevance in folksonomies.
SPSBOS -- How your metadata strategy impacts everything you doChristian Buckley
Presentation given 4-9-2011 at SharePoint Saturday Boston on the need for sound metadata and taxonomy strategy in any SharePoint deployment (or re-architecture).
This document summarizes a research paper that proposes a new algorithm for influence maximization in social networks. The algorithm draws inspiration from previous works on community detection and a data-based credit distribution model. It first assigns credits to users based on their past actions to determine probabilistic influence between users. It then uses a community detection approach to identify groups of similar users before applying an influence maximization algorithm based on the independent cascade model. The proposed approach aims to better learn mutual influence from user data and improve time complexity by leveraging the relationship between community detection and viral marketing.
This document provides an overview of managed metadata and taxonomies in SharePoint 2010. It discusses metadata definitions and usage scenarios, folksonomies versus formal taxonomies, taxonomy management features, content type hubs for sharing content across sites, and configuration considerations. The presentation includes demonstrations of tagging, term sets, and content type publishing capabilities in SharePoint 2010.
The document summarizes Jennifer's experiences during a week-long outdoor trip with her grade 9 class. Some of the activities included rock climbing, caving, teaching at a local school, biking, team building exercises, visiting a family home, and camping. Each activity helped develop different social-emotional learning skills. Rock climbing required complex problem solving while caving promoted personal management and collaboration. Teaching at the school tested skills like collaboration, citizenship, and leadership. The camping trip concluded with a challenging tent building exercise that strengthened collaborative work.
Innovation is Everywhere - Lebanon innovation ecosystemAgence Tesla
The document provides a history and overview of the innovation ecosystem in Lebanon. It discusses how French missionaries established schools in Lebanon after World War 1 which helped develop the political and business elite. More startups emerged in 2010 and 2012, and funds increased in 2013-2014 including a $400 million initiative by the Central Bank. Lebanon has a large diaspora population and faces challenges from infrastructure costs and regional instability, but benefits from an open population prone to innovation. Top connectors in the ecosystem include Arabnet founder Omar Christidis and the Beirut Digital District helps connect startups. Moving forward, Lebanon aims to attract more talent and better connect with its global diaspora.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/classmanagement
The document discusses how personalization and dynamic content are becoming increasingly important on websites. It notes that 52% of marketers see content personalization as critical and 75% of consumers like it when brands personalize their content. However, personalization can create issues for search engine optimization as dynamic URLs and content are more difficult for search engines to index than static pages. The document provides tips for SEOs to help address these personalization and SEO challenges, such as using static URLs when possible and submitting accurate sitemaps.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
Aiim Webinar Helen Mitchell Unified Search Final 7 21 2010Helen Mitchell
The document discusses search technologies and strategies for providing a unified view of private and public information across an organization. It covers definitions of key search concepts, challenges of information overload, examples of enterprise search, federated search, vertical search and summarization tools, as well as best practices and technologies to consider for unified search.
This document summarizes research on social capital among Iraqis conducted in 2005. The researchers administered a 192-item survey in Basra, Iraq and the Netherlands to measure trust and perceptions of ethnic/religious threats within Iraqi social networks. The researchers found relationships and trust were related to patterns of social capital. Perceptions of outgroups seemed related to social capital resources. The researchers shared their findings widely via email and the internet, attracting up to 350 readers/contributors daily. This rapid dissemination allowed them to quickly share timely research with stakeholders. The researchers conclude internet-facilitated communication can increase the visibility and impact of research.
The document describes the Semantic Communication Engine Innsbruck (SCEI), a software suite that supports online communication, feedback collection, and impact measurement across multiple channels. It introduces key terms and defines the problem of managing content distribution across different online channels. The proposed solution features a semantic layer that abstracts domain concepts from specific channels, and a "weaving process" that aligns content with channels. The architecture separates the software into a content management system and a distribution component called dacodi, which uses adapters to interface with individual channels in a standardized way.
Everything Self-Service:Linked Data Applications with the Information WorkbenchPeter Haase
The document discusses an information workbench platform that enables self-service linked data applications. It addresses challenges in building linked data applications like data integration and quality. The platform allows for discovery and integration of internal and external data sources. It provides intelligent data access, analytics, and collaboration tools through a semantic wiki interface with customizable widgets. Example application areas discussed are knowledge management, digital libraries, and intelligent data center management.
Findability Primer by Information Architected - the IA Primer SeriesDan Keldsen
The document discusses the importance of findability in the digital age. It defines findability as "the art and science of making content findable" and distinguishes it from simple search functions. Findability utilizes various technologies and techniques to help users efficiently locate relevant information among large volumes of digital content. These include tagging, taxonomies, semantic search, and natural language processing. The document provides an overview of different findability component technologies and their applications.
This document discusses managed metadata and taxonomies in SharePoint 2010. It defines key terms like metadata, taxonomy, and folksonomy. It provides examples of how an organization's information architecture can evolve over time from simple document storage to a more complex taxonomy. Best practices are suggested for designing taxonomies, including considerations for dynamic external tags, security, open vs. closed term sets, and content type hubs. Programming the managed metadata service in SharePoint 2010 is also briefly covered.
Rep on the Roll A peer to peer reputation system based on a rolling blockchainRichard Dennis
This document summarizes a paper that proposes a new blockchain-based reputation system called "Rep on the Roll" to address limitations of existing systems. It discusses how current centralized reputation systems used by e-commerce sites are not suitable for decentralized peer-to-peer networks. Existing P2P reputation systems have issues like optional participation and lack of identity management. The proposed system aims to solve problems like sybil attacks and improve scalability by reducing blockchain size by 92%, addressing a key challenge for all blockchain networks.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
This document discusses how metadata can be used to protect and derive value from content stored in public and private clouds. It proposes the concept of "Guardian Angels" that collect individualized metadata about a user's interactions with content. An "Invisible College" would allow anonymous aggregation of metadata from Guardian Angels to determine emergent meanings while preserving privacy. Standards are needed to incorporate these concepts and allow organic growth of associative metadata to enhance cloud services and information assets.
This document discusses how taxonomies and ontologies can improve enterprise search capabilities. It provides examples from case studies of organizations in the military, retail, and financial sectors. The case studies demonstrate how developing taxonomies, ontologies, content types and metadata structures helped organizations better classify, search and retrieve unstructured content to meet business needs.
Information Architecture Primer - Integrating search,tagging, taxonomy and us...Dan Keldsen
This document discusses the importance of taxonomy and classification within an information architecture. It defines key terms like taxonomy, thesaurus, ontology, and classification. It explains that taxonomy and classification help address the eternal problems of effectively cataloging and retrieving unstructured information. The document also discusses challenges like ambiguity, multiple meanings of words, and the importance of browsing versus searching in navigating large amounts of information.
The Web Information System of the National Institute for Astrophysics: differ...inscit2006
Caterina Boccato and Serena Pastore
National Institute for Astrophysics, Astronomical Observatory of Padova, Vicolo Osservatorio 5, 35122, Padova, ITALY
Towards enhanced user interaction to qualify web resources for higher-layered...Monika Steinberg
The document discusses enhancing user interaction to qualify web resources for knowledge transfer applications. It proposes a three-level model for assessing resource quality: first through metadata analysis, second through user interaction such as questionnaires, and third through intelligent analysis. It suggests that game-based interaction could motivate ongoing user participation in rating and ranking resources. An example uses a tag-based game on Flickr to increase image relevance in folksonomies.
SPSBOS -- How your metadata strategy impacts everything you doChristian Buckley
Presentation given 4-9-2011 at SharePoint Saturday Boston on the need for sound metadata and taxonomy strategy in any SharePoint deployment (or re-architecture).
This document summarizes a research paper that proposes a new algorithm for influence maximization in social networks. The algorithm draws inspiration from previous works on community detection and a data-based credit distribution model. It first assigns credits to users based on their past actions to determine probabilistic influence between users. It then uses a community detection approach to identify groups of similar users before applying an influence maximization algorithm based on the independent cascade model. The proposed approach aims to better learn mutual influence from user data and improve time complexity by leveraging the relationship between community detection and viral marketing.
This document provides an overview of managed metadata and taxonomies in SharePoint 2010. It discusses metadata definitions and usage scenarios, folksonomies versus formal taxonomies, taxonomy management features, content type hubs for sharing content across sites, and configuration considerations. The presentation includes demonstrations of tagging, term sets, and content type publishing capabilities in SharePoint 2010.
The document summarizes Jennifer's experiences during a week-long outdoor trip with her grade 9 class. Some of the activities included rock climbing, caving, teaching at a local school, biking, team building exercises, visiting a family home, and camping. Each activity helped develop different social-emotional learning skills. Rock climbing required complex problem solving while caving promoted personal management and collaboration. Teaching at the school tested skills like collaboration, citizenship, and leadership. The camping trip concluded with a challenging tent building exercise that strengthened collaborative work.
Innovation is Everywhere - Lebanon innovation ecosystemAgence Tesla
The document provides a history and overview of the innovation ecosystem in Lebanon. It discusses how French missionaries established schools in Lebanon after World War 1 which helped develop the political and business elite. More startups emerged in 2010 and 2012, and funds increased in 2013-2014 including a $400 million initiative by the Central Bank. Lebanon has a large diaspora population and faces challenges from infrastructure costs and regional instability, but benefits from an open population prone to innovation. Top connectors in the ecosystem include Arabnet founder Omar Christidis and the Beirut Digital District helps connect startups. Moving forward, Lebanon aims to attract more talent and better connect with its global diaspora.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/classmanagement
The document discusses how personalization and dynamic content are becoming increasingly important on websites. It notes that 52% of marketers see content personalization as critical and 75% of consumers like it when brands personalize their content. However, personalization can create issues for search engine optimization as dynamic URLs and content are more difficult for search engines to index than static pages. The document provides tips for SEOs to help address these personalization and SEO challenges, such as using static URLs when possible and submitting accurate sitemaps.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
This document summarizes a study of CEO succession events among the largest 100 U.S. corporations between 2005-2015. The study analyzed executives who were passed over for the CEO role ("succession losers") and their subsequent careers. It found that 74% of passed over executives left their companies, with 30% eventually becoming CEOs elsewhere. However, companies led by succession losers saw average stock price declines of 13% over 3 years, compared to gains for companies whose CEO selections remained unchanged. The findings suggest that boards generally identify the most qualified CEO candidates, though differences between internal and external hires complicate comparisons.
Identical Users in Different Social Media Provides Uniform Network Structure ...IJMTST Journal
The primary point of this venture is secure the client login and information sharing among the interpersonal organizations like Gmail, Face book and furthermore find unknown client utilizing this systems. On the off chance that the first client not accessible in the systems, but rather their companions or mysterious client knows their login points of interest implies conceivable to abuse their talks. In this venture we need to defeat the mysterious client utilizing the system without unique client information. Unapproved client utilizing the login to talk, share pictures or recordings and so on. This is the issue to be overcome in this venture .That implies client initially enlist their subtle elements with one secured question and reply. Since the unknown client can erase their talk or information. In this by utilizing the secured questions we need to recuperate the unapproved client talk history or imparting subtle elements to their IP address or MAC address. So in this venture they have discovered an approach to keep the mysterious clients abuse the first client login points.
Personalized E-commerce based recommendation systems using deep-learning tech...IAESIJAI
As technology is surpassing each day, with the variation of personalized drifts
relevant to the explicit behavior of users using the internet. Recommendation
systems use predictive mechanisms like predicting a rating that a customer
could give on a specific item. This establishes a ranked list of items according
to the preferences each user makes concerning exhibiting personalized
recommendations. The existing recommendation techniques are efficient in
systematically creating recommendation techniques. This approach
encounters many challenges such as determining the accuracy, scalability, and
data sparsity. Recently deep learning attains significant research to enhance
the performance to improvise feature specification in learning the efficiency
of retrieving the necessary information as well as a recommendation system
approach. Here, we provide a thorough review of the deep-learning
mechanism focused on the learning-rates-based prediction approach modeled
to articulate the widespread summary for the state-of-art techniques. The
novel techniques ensure the incorporation of innovative perspectives to
pertain to the unique and exciting growth in this field.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
This document describes a proposed algorithm for improving recommendation systems for e-services. It involves the following key steps:
1. Clustering customer transaction histories to group similar purchase patterns and derive customer-based recommendations.
2. Using incremental association rule mining on the transaction data to detect frequently purchased item sets and relationships between items.
3. Developing a fuzzy model to classify customers and provide dynamic recommendations tailored to different customer types. The recommendations will be based on matching customer preferences and purchase histories to specific product sets.
4. The algorithm clusters transactions, mines association rules incrementally as new data is added, and generates recommendations by classifying customers and matching them to relevant product clusters. This provides a personalized and
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
This document outlines Thomas Liggett's stakeholder network assessment methods and tools. It describes how he builds baseline models for clients within 30 days and assesses key performance indicators. It also explains how he models individual stakeholders like persons and companies, analyzing how they are influenced by various interests and relationships. Through proprietary tools like the Performance Visualization System, he can map these networks and overlay models to evaluate alignment and prove the impact on performance.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
A Community Detection and Recommendation SystemIRJET Journal
This document proposes a community detection and recommendation system that uses community detection algorithms to analyze social networks and extract friendship relationships between users. It will develop the approach using the MapReduce framework to improve the scalability, coverage, and cold start issues of collaborative filtering recommendation systems. The system aims to provide more accurate recommendations by incorporating social network information and trust between users into the recommendation process.
IRJET- Analysis on Existing Methodologies of User Service Rating Prediction S...IRJET Journal
This document summarizes and analyzes existing methodologies for user service rating prediction systems. It discusses recommendation systems including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering predicts user ratings based on opinions of other similar users but faces challenges of cold start, scalability, and sparsity. Content-based filtering relies on item profiles and user preferences to recommend similar items but requires detailed item information. Hybrid systems combine collaborative and content-based filtering to address their individual limitations. The document also examines social recommender systems and how they can account for relationship strength, expertise, and user similarity within social networks.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Study of Recommendation System Used In Tourism and Travelijtsrd
This study is based on Recommendation Systems and its Types used in Tourism and Travel Website. Recommendation Systems are used in websites so that it can recommend item to a user based on his her interest and on the basis of user profile. In this paper, I design a recommender system for recommending tourist places based on content based and collaborative filtering techniques. This method combines both behavioural and content aspects of recommendations. The flow for the research is that first of all using cosine similarity, weighted ratings and Location APIs we build a content based system. The process is carried out by comparing the features of the item with respect to the user’s preferences. Then followed by collaborative filtering techniques such as Correlation and K Nearest neighbour in which items predict the interest of the user on an activity considering the evaluation that a particular user has given to similar activities. Shikhar "Study of Recommendation System Used In Tourism and Travel" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47922.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/47922/study-of-recommendation-system-used-in-tourism-and-travel/shikhar
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
with current projections regarding the growth of
Internet sales, online retailing raises many questions about how
to market on the Net. A Recommender System (RS) is a
composition of software tools that provides valuable piece of
advice for items or services chosen by a user. Recommender
systems are currently useful in both the research and in the
commercial areas. Recommender systems are a means of
personalizing a site and a solution to the customer’s information
overload problem. Recommender Systems (RS) are software
tools and techniques providing suggestions for items and/or
services to be of use to a user. These systems are achieving
widespread success in ecommerce applications now a days, with
the advent of internet. This paper presents a categorical review
of the field of recommender systems and describes the state-ofthe-
art of the recommendation methods that are usually
classified into four categories: Content based Collaborative,
Demographic and Hybrid systems. To build our recommender
system we will use fuzzy logic and Markov chain algorithm.
The document proposes a multi-tier sentiment analysis system called MSABDP to analyze large-scale social media data more efficiently. MSABDP uses Hadoop for its distributed processing and storage capabilities. It collects Twitter data using Apache Flume and stores it in HDFS. It then applies a multi-tier classification approach combining lexicon-based and machine learning techniques to classify tweets into multiple sentiment classes, reducing complexity compared to single-tier architectures. Evaluation on real Twitter data showed MSABDP improved classification accuracy over single-tier approaches by 7%.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...
Recsys virtual-profiles
1. Generating Supplemental Content Information Using
Virtual Profiles
Haishan Liu
Linkedin Corporation
2029 Stierlin Court
Mountain View, CA, 94043
haliu@linkedin.com
Mohammad Amin
Linkedin Corporation
2029 Stierlin Court
Mountain View, CA, 94043
mamin@linkedin.com
Baoshi Yan
Linkedin Corporation
2029 Stierlin Court
Mountain View, CA, 94043
byan@linkedin.com
Anmol Bhasin
Linkedin Corporation
2029 Stierlin Court
Mountain View, CA, 94043
abhasin@linkedin.com
ABSTRACT
We describe a hybrid recommendation platform/technique
at LinkedIn that seeks to optimally extract relevant infor-
mation pertaining to items to be recommended. By extend-
ing the notion of an item profile, we propose the concept
of a “virtual profile” that augments the content of the item
with rich set of features inherited from members who have
already shown explicit interest in it. Unlike item-based col-
laborative filtering, we focus on discovering the characteris-
tic descriptors that underlie the item-user association. Such
information is used as supplemental features in a content-
based filtering system. The main objective of virtual pro-
files is to provide a means to tap into rich-content infor-
mation from one type of entity and propagate features ex-
tracted from which to other affiliated entities that may suf-
fer from relative data scarcity. We empirically evaluate the
proposed method on a real-world community recommenda-
tion problem at Linkedin. The result shows that the virtual
profiles outperform a collaborative filtering based approach
(user who likes this also likes that). In particular, the im-
provement is more significant for new users with only limited
connections, demonstrating the capability of the method to
address the cold-start problem in pure collaborative filtering
systems.
Categories and Subject Descriptors
H.2.8 [Database Management]: Data Mining
General Terms
Theory
Keywords
hybrid recommender systems, feature generation and extrac-
tion, model-based recommendation, virtural profiles
1. INTROCUDTION
Large scale recommender systems, in the era of internet scale
data deluge, contribute significantly to mitigate information
overload problem by unveiling relevant and interesting ob-
jects to users. Rather than hoping for serendipitous encoun-
ters, recommender systems bring forth the notion of per-
sonalized information discovery by presenting to the user a
smaller pool of relevant objects. Collaborative filtering, the
de facto mechanism for recommendation, fails to address
“cold start problems” which has led to the exploration of
hybrid recommenders. Hybrid recommenders combine in-
formation obtained from different sources and techniques to
achieve better outcome. Typically a hybrid recommender
system incorporates information from a myriad of sources
e.g. content meta data, interaction data, global popularity,
social network and social interaction information and so on.
Each of these information sources offers different level of rel-
evance guarantee at varying computation overhead. Hence,
how these information sources are computed and how they
are combined play a vital role in the final outcome.
As of today LinkedIn has more than 220 million users. As
the largest and most popular professional networking site,
LinkedIn presents some unique opportunities and challenges
for content discovery and recommendation. It is imperative
for the members to be able to discover and subscribe to
companies and groups (referred to as community henceforth)
that might be relevant to them from a professional context.
In this paper, we describe a hybrid community recommen-
dation platform/technique at LinkedIn that optimally com-
bines information from multiple sources. In order to extract
more relevant information pertaining to the community to
be recommended, i.e. to further extend the notion of content
meta data, we have proposed the concept of “virtual profile”
that augments the content meta data with rich set of features
inherited from the set of members who have already shown
explicit interest to it. In general the notion of virtual profile
answers: “What are the most dominant features pertain-
ing to the members who have shown interest to a particular
2. community?”. This question essentially maps an object into
the same feature space as that of the subscribers’. Content
meta data, extended with this inferred information provides
additional warranty against cold start problem. LinkedIn
data presents a unique opportunity to extend the content
features with extracted features since there is no dearth of
rich set of information about the subscribers in the data set,
which essentially renders the synergy immensely valuable.
The contribution of this paper is as follows:
1. Generic content meta data extension method i.e vir-
tual profile generation.
2. Scalable and generic recommendation computation plat-
form that powers multiple real-time recommendation
products at LinkedIn.
3. Seamless integration of multiple, heterogeneous data
sources to compute optimal outcome.
2. RELATED WORK
There has been a flurry of research in the domain of recom-
mender systems with the objective of improving personal-
ization [1]. Most traditional recommenders are powered by
collaborative filtering [9, 17], content-based predictors [8,
14] and knowledge based filtering techniques [11]. Each in-
dividual techniques have their own strengths and weaknesses
e.g. while collaborative filtering techniques suffer from data
sparsity and cold start problems [15], content-based tech-
niques are prone to skewed recommendation [14]. Hybrid
recommenders combine the best of both worlds, making the
recommenders more robust in practice. Much work has been
done to combine multiple recommenders in an effective way
to outperform any single one. In [5] Burke depicts a taxon-
omy of recommender systems, where multiple recommenders
are arranged to allow execution in a parallel or cascaded
topology. A system described in [4] combines multiple col-
laborative filtering approaches using a linear combination of
static weights learned via linear regression. STREAM [2],
which combines multi-tier predictors, uses dynamically gen-
erated metrics to learn the next level of predictors. In [12],
a hybrid movie recommender system is proposed that uses
content based predictors to boost user data which drives the
ensuing collaborative filtering based recommendation. The
content information is obtained from IMDB and a Naive
Bayes classifier is used for building user item profiles. Fi-
nally a user-based collaborative filtering is employed to ob-
tain the final recommendation. However, this approach suf-
fers from scalability issues. Pazzani [13] proposed a hybrid
recommender system where the content based user profiles
are used to group similar users which is subsequently used
to predict user preferences. In many of these user-item rec-
ommendation frameworks, items to be recommended can be
augmented with meta-data corresponding to the members
who have already shown explicit interest to it. In other
words, these items can be represented as an object in the
same feature space as that of the users. These representa-
tions could be thought of “virtual user profiles” or “virtual
profiles”. This could potentially add one other layer of in-
formation source to guide the recommendation process. In
our approach, we describe a large scale recommender system
that combines data from multiple heterogeneous sources in-
cluding virtual profiles and social network to serve real time
traffic in a large professional social networking site.
3. METHOD
3.1 System Overview
We adhere to building our recommender system based on
content filtering since we have an abundant access to rich-
content entities, such as user profiles, which enables a straight-
forward means for feature extraction, indexing and match-
ing. Target entities (those the client wants recommenda-
tions of) are feature extracted and put into a reverse index,
and source entities (those the client wants recommendations
for) are converted into complex queries against the index.
This provides a form of content-based recommendation score
where the match is determined by the degree of similarity
between the source and target entity features, with differ-
ent fields weighted by a set of parameters determined by an
offline learning-to-rank process. Figure 1 illustrates a brief
workflow of the system. It also shows how we can augment
the system by including more information, such as virtual
profiles, as new features in the content filtering recommen-
dation, as detailed below.
Figure 1: A brief workflow for the recommender
system with virtual profiles.
We view every entity as being characterized by two set of
content features: one extracted from explicit information
associated with the entity which we name the “primary pro-
file”, and the other inferred from the entity’s behavior and
association with other entities, which we name the “virtual
profile.”The main objective of virtual profiles is to provide a
means to tap into rich-content information from one type of
entity and propagate features extracted from which to other
affiliated entities that may suffer from relative data scarcity.
Essentially, a virtual profile of an entity is an aggregation
of statistically relevant features from primary profiles of af-
filiated entities, in which way it introduces a collaborative
3. filtering aspect in our content filtering system. For example,
a virtual profile of a Linkedin group constitutes distinctive
features from its participants so that the group can be most
effectively distinguished from others.
To first extract features from entities to generate primary
profiles, we utilize a feature extractor layer, a standalone
service that accumulates underlying entity database change
events and identifies various fields in the document. Various
types of fields that could be feature extracted include rich
text fields, such as job summary, member position summary
etc., and specialized fields, such as Geo entities including
region, country, city, coordinates, etc.
The presented content filtering system can be extended to
consider other collaborative filtering aspects, for example,
by including network proximity as a feature while computing
relevance scores. We describe a browsemap-based method
along this line as a comparison in Section 4. As a gen-
eral platform, every application consuming recommenda-
tions from this system can easily build its own logic for
reranking/reordering of results based on custom filtering cri-
teria. the concept of network proximity, e.g., recommending
jobs to discussion groups.
3.2 Generating Virtual Profiles
The virtual profile generation process for an entity aims at
selecting from a total of n features of its affiliated entities,
a subset with k < n features that is “maximally informa-
tive” about the entity. In a classification point of view, the
entity that we generate the virtual profile for represents a
target class for a set of documents (primary profiles). We
need a measure to evaluate the“information content”of each
individual feature with regard to the target class. We pro-
pose to use mutual information for this purpose. Mutual
information measures arbitrary dependencies between ran-
dom variables. And the fact that the mutual information
is independent of the coordinates chosen permits a robust
estimation makes it suitable for assessing the “information
content” of features in complex classification tasks.
In accordance with Shannon’s information theory, the un-
certainty of a document class C as a random variable can
be measured as:
H(C) = −
∑
c∈C
P(c)logP(c) ,
After knowing the feature vector F, the conditional entropy
H(C|F) measures the remaining uncertainty about C:
H(C) = −
∑
f∈F
P(f)
∑
c∈C
P(c|f)logP(c|f) .
After having observed the feature vector F, the mutual in-
formation, i.e., the amount of decreased class uncertainty is
defined as:
I(C; F) = H(C) − H(C|F) =
∑
c,f
P(c, f)log
P(c, f)
P(c)P(f)
,
where P(c, f) is the joint probability of class c and feature
f.
Therefore, to generate virtual profiles, the goal is to find
the optimal feature subset, S ⊆ F, so that I(C; S) is max-
imized. From an information theoretic perspective, select-
ing features that maximize I(C; F) translates into selecting
those features that contain the maximum information about
class C. However, locating the optimal subset requires an
exhaustive combinatorial search over the feature space, re-
quiring a number of runs equal to
(n
k
)
, where n is the size
of the original feature set and k is that of the desired sub-
set. Besides, an exact solution also demands large training
sample sizes to estimate the higher order joint probability
distribution in I(F; C). For example, Fraser’s method [6], a
computationally efficient algorithm for calculating the opti-
mal I(C; S), requires for its convergence a number of sam-
ples “in the millions” when the number of features in the
input vector is larger than 3 or 4.
Given these difficulties, most of the existing approaches ap-
proximate I(F; C) based on the assumption of lower-order
dependencies between features. For example, a second-order
feature dependence assumption is proposed by Battiti [3]
to approximate I(F; C) by a greedy incremental selection
scheme with a heuristic to account for correlations between
features: Given a set of already selected features, the algo-
rithm chooses the next feature as the one that maximizes the
information about the class corrected by subtracting a quan-
tity proportional to the average mutual information with the
selected features.
Unfortunately, the calculation of pairwise feature correlation
I(f, f′
) is impractical in our case because the feature dimen-
sion is extremely high given the bag-of-words extracted from
textual contents. Therefore, we make a first-order class de-
pendence assumption that each feature independently influ-
ences the class variable, which means to select the mth fea-
ture, fm, is independent from the (m − 1) already selected
features, i.e., P(fm|f1, . . . , fm−1, C) = P(fm|C). This re-
sults a straightforward greedy algorithm to generate the vir-
tual profile for an entity c, which consists of following steps:
1) gather features from all primary profiles associated with
entities that have an affiliation with c, 2) calculate mutual
information, I(f; c), between each feature and e, and 3) se-
lect top k features with highest I(f; c) into the virtual pro-
file. More specifically, I(f; c) can be calculated as follows.
I(f; c) =
∑
ef ∈{1,0}
∑
ec∈{1,0}
P(f = ef , c = ec) log
P(f = ef , c = ec)
P(f = ef )P(c = ec)
,
(1)
where f is a random variable that takes values ef = 1 (en-
tity primary profile contains feature f) and ef = 0 (the
entity primary profile does not contain feature f), and c is
a random variable that takes values ec = 1 (the entity is
affiliated with c) and ec = 0 (the entity is not affiliated with
c). The probabilities in Equation 1 can be calculated using
maximum likelihood estimation.
4. EXPERIMENTS
4. Our goal is to test if virtual profiles are a valuable source
of features to improve the recommendation performance. In
designing experiments, we want to verify the heuristic as-
sumption that virtual profile can use features greedily se-
lected by mutual information. We also want to compare the
performance of virtual profiles with other classic collabora-
tive filtering methods and study their tradeoffs. Further-
more, by experimenting with different parameter settings
to generate virtual profiles, we want to provide a general
guidance on how virtual profiles can be best implemented in
practice.
4.1 Methodologies
We choose a community recommendation problem at Linkedin
as the test application. Successful recommendations would
result in users following certain communities, while users
are also presented the choice to opt-out communities at any
later point.
We extract three kinds features from entities (users and com-
munities) in this application domain as follows.
1. content features: features from users’ and communi-
ties’ textual information extracted into predefined stan-
dardized fields (e.g., name, industry, description, etc.).
2. virtual profile: as described in Section 3, a set of fea-
tures selected from a community’s followers as supple-
ments to the community’s primary profile.
3. browsemap: a collaborative feature representing the
co-affiliation relationship, or “users who follow X also
follow Y.”
Browsemaps capture a notion of similarity between com-
munities that is driven by users’ preference. To generate
a browsemap for a community, from all other communi-
ties that it shares followers with, we choose top 50 ones
ranked by TF/IDF. And then for each user, we take the
closure of communities she has already followed with re-
spect to browsemaps, and select top 50 ones weighted by
their TF/IDF scores normalized over the number of com-
munities followed. Communities selected in this way can be
essentially seen as recommendations by collaborative filter-
ing. We instead treat them as part of a standalone feature,
and when combined with users’ content features to generate
a search query, it would lead to extra field matches with hits
against communities appear in the feature. And the weight
of this match, just like matches in other features, can be
determined in an offline learning process.
The content features extracted for communities contains
only three fields (i.e., name, description, and tags). They
represent nearly a minimum amount of information that is
required for a content filtering recommender system to func-
tion, and are therefore considered as a baseline in the exper-
iment. Browsemaps, on the other hand, are designed as an
alternative to virtual profiles for comparison, given that they
both take into account the interaction among entities.
As for model fitting, we use a training set including 3.4
million positive and 2.2 million negative examples gathered
from both explicit and implicit user feedbacks (e.g., fol-
low/unfollow or lack of action to recommendations). We
apply an L2-regularized logistic regression with various com-
bination of the above mentioned features. The best model
under each configuration is selected by optimizing the area
under the ROC curve (AUC-ROC). Performances of differ-
ent models are evaluated both offline and online. The results
are presented in the next sections.
4.2 Results
4.2.1 Offline evaluation
We compare the AUC for models obtained by training with
four different feature configurations, namely, (A) content
features only, (B) content features plus virtual profiles, (C)
content features plus browsemaps, and (D) content features
plus both virtual profiles and browsemaps. It can be seen
from Figure 2 that, the ROC curve of model A completely
dominates that of model B (with AUCs 0.72 vs. 0.60), and
both of them dominate that of model C (AUC 0.44). The
same performance pattern is also exhibited in the precision-
recall curve, as shown in Figure 3.
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
False positive rate
Truepositiverate
content features + vp
content features + bm
content features + vp + bm
content features only
Figure 2: ROC curves for different models.
Besides classification performance, another important mea-
sure that can be evaluated offline is the coverage, which
refers to the degree to which recommendations cover the set
of available items (item space coverage) and the degree to
which recommendations can be generated to all potential
users (user space coverage) [7, 10]. Owing to a distributed
algorithm developed at Linkedin, we are able to calculate
recommendations offline for all our 220 million users. Us-
ing each of the trained model described above, we calculate
a different set of recommendations for each user, with the
size of each set capped at 50. We counted numbers of times
5. 0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Recall
Precision
content features + vp
content features + bm
content features + vp + bm
content features only
Figure 3: Precision-recall curves for different mod-
els.
unique communities appeared in recommendations (frequen-
cies) under different models. Figure 4 shows a logarithmic
scale of the frequencies sorted in descending order plotted
against their ranks.
It is not surprising to see that the baseline curve from the
content-features-only model is the lowest since features ex-
tracted for communities in this case contains the least amount
of information. And the distribution of the recommendation
frequency simply reflects the distribution of the amount of
textual content of each community, which is subject to the
power law. On the other hand, the curve from the model
with the addition of browsemaps visibly bulges outwards
from the baseline for about two thirds of points, indicating
that those points are getting higher frequencies showing up
in recommendations, hence more coverage. Most remark-
ably, the model with the addition of virtual profiles signifi-
cantly increased the frequencies for almost all points on the
curve except for cases where original baseline frequencies are
extremely high or low.
The reason why browsemaps slightly boost the coverage for
some communities is because those communities bear little
content information yet having followers already. Having
followers makes them eligible to be potentially included in
other communities’ browsemaps, and thus leads to a higher
chance to matches with users. However, for users not hav-
ing followed any communities at all, browsemaps become an
empty feature, which is the reason why for about a third of
communities, there sees no increase in coverage from browsemaps
compared with the baseline. This phenomenon is also illus-
trated in Figure 5, in which the recommendation frequencies
of unique companies are only counted for new users (i.e.,
users who have not started following communities yet). We
observe that the model with browsemaps produces an iden-
tical curve to the baseline, while the model with virtual pro-
files exerts a consistent boost. This shows that browsemaps,
as a feature of a collaborative filtering aspect, fails to address
cold start, while virtual profiles provides a well-rounded im-
provement in terms of both coverage and predictive power.
0 50000 100000 150000 200000 250000
2e+025e+022e+035e+032e+045e+042e+05
numberofrecommendations
content features + vp
content features + bm
content features only
Figure 4: number of recommendation per unique
companies.
4.2.2 Online evaluation
To further evaluate models with various feature configura-
tions (i.e., content features with vp, content features with
bm, content features with both vp and bm, and content fea-
tures only), we deployed them to serve realtime online rec-
ommendation requests and compare performances through
a bucket test. We assign a unique bucket of 2.5% randomly
selected users to each model. The bucket with the model
based only on content features is the control, while others
are variants.
The duration of the test is determined according to Wheeler [18],
where a conservative estimation of sample size to achieve an
80% power (the probability of correctly rejecting the null
hypothesis when it is indeed false) is given by Equation 2.
n = (
4rσ
∆
)2
, (2)
where n is the minimum number of samples (impressions to
be delivered) for each equal-sized variant, r is the number
of variants, σ2
is the variance of the OEC (Overall Evalu-
ation Criterion [16], a quantitative measure of the experi-
ment’s objective.), and ∆ is the sensitivity, or the desired
amount of change. The OEC in this test is the Click-through
rate (CTR) of recommendations. Assume each click-through
6. 0 50000 100000 150000 200000
2e+025e+022e+035e+032e+045e+04
numberofrecommendations
content features + vp
content features + bm
content features only
Figure 5: number of recommendation for new users
per unique companies.
event is a Bernoulli trial with probability p = ctr0 (con-
trol CTR, which is estimated from historical data), then
σ2
= p(1 − p). Applying Equation 2 and knowing the ap-
proximate recommendation impressions per day, we derive
the length of the test to be 7 days.
Figure 7 presents the results of the test by showing the per-
centage change in CTR of variant models relative to the con-
trol, on each individual day of the test. Overall, the model
with virtual profiles outperforms the control by 91.2%. Sur-
prisingly, however, we do not observe any improvement from
the model with browsemaps. The model with both virtual
profiles and browsemaps increased the CTR by 84.4%. The
difference between the two best performing model is not
significant (p value 0.062), which is similar to the offline
evaluation result. The reason why browsemaps fail to in-
crease overall CTR may be attributed to the fact that only
one third of users have followed communities in this par-
ticular application, meaning the cold start effect is much
pronounced. Virtual profiles, on the other hand, is not vul-
nerable to this problem since it is content-based and does
not rely on pre-existing user-item affiliations, as is demon-
strated in this experiment.
5. CONCLUSION AND FUTURE WORK
We presented virtual profiles, a generic content meta data
extension method. We also introduced how it is utilized in a
scalable and generic content-based hybrid recommender sys-
tem that powers multiple real-time recommendation prod-
ucts at LinkedIn. The goal of virtual profiles is to provide a
means to tap into rich-content information from one type of
entity and propagate features extracted from which to other
affiliated entities that may suffer from relative data scarcity.
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
False positive rate
Truepositiverate
vp−top50
vp−top100
vp−top200
Figure 6: ROC curves for virtual profiles with dif-
ferent number of terms.
It brings a collaborative filtering aspect in the form of a sup-
plement to content features in the recommender system. It
is shown to outperform a method that directly incorporate
network proximity from collaborative filtering.
Experiments supported that our first-order class dependence
assumption and the greedy algorithm in calculating the mu-
tual information is a reasonable approximation. In future
work, we will investigate scalable ways to account for de-
pendencies among features. We plan to explore more term
weighting methods besides mutual information, including
other classic information theoretic quantities such as the
Kullback-Leibler divergence, or TF/IDF.
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