R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
This document discusses community analysis in social media mining. It defines social media communities as groups of users who form links and interact based on common interests. Community detection aims to discover these implicit communities through algorithms. Member-based detection examines node characteristics like degree and similarity, while group-based detection finds communities with properties like being balanced, robust, modular, dense, or hierarchical. Analyzing communities provides insight into user interactions and behaviors that are only observable at a group level.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.
This document discusses extracting communities from web archives over time. It begins by defining key terms used, such as the web community chart and notations for time periods and communities. It then describes types of changes that can occur to communities over time, such as emerging, dissolving, growing, shrinking, splitting, and merging. It also defines metrics to measure a community's evolution, such as growth rate, stability, disappearance rate, and merge rate. The document explains how web archives are used to build web graphs and extract community structures over multiple time periods to analyze how the community structure changes dynamically over time.
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
This document discusses community analysis in social media mining. It defines social media communities as groups of users who form links and interact based on common interests. Community detection aims to discover these implicit communities through algorithms. Member-based detection examines node characteristics like degree and similarity, while group-based detection finds communities with properties like being balanced, robust, modular, dense, or hierarchical. Analyzing communities provides insight into user interactions and behaviors that are only observable at a group level.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
This document discusses data mining techniques and recommendation systems. It describes common data mining techniques like classification, clustering, regression, association rule mining and outlier analysis. It also discusses the knowledge discovery process and applications of data mining. The document then covers recommendation systems, describing content-based, collaborative filtering and hybrid recommendation approaches. It provides examples of these systems.
This document discusses extracting communities from web archives over time. It begins by defining key terms used, such as the web community chart and notations for time periods and communities. It then describes types of changes that can occur to communities over time, such as emerging, dissolving, growing, shrinking, splitting, and merging. It also defines metrics to measure a community's evolution, such as growth rate, stability, disappearance rate, and merge rate. The document explains how web archives are used to build web graphs and extract community structures over multiple time periods to analyze how the community structure changes dynamically over time.
This document discusses community detection in social media and online networks. It defines communities as groups of densely interconnected nodes in a graph. It outlines various algorithms for detecting communities, including graph partitioning, k-clique detection, core decomposition, divisive algorithms based on edge centrality, and modularity maximization approaches. It also discusses local community detection methods and evaluation of community detection results.
The document discusses social recommender systems and how they can improve on traditional collaborative filtering approaches by incorporating trust relationships between users. It outlines research that used trust propagation algorithms to make recommendations for cold start users who lack sufficient rating histories. The author proposes to further explore how different types of social relationships (e.g. trust, friendship) differentially impact recommendation performance and to evaluate social and similarity-based collaborative filtering approaches.
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Graph theory concepts like centrality, clustering, and node-edge diagrams are used to analyze social networks. Visualization techniques include matrix representations and node-link diagrams, each with advantages. Hybrid representations combine these to leverage their strengths. MatrixExplorer allows interactive exploration of social networks using both matrix and node-link views.
The document discusses the emergence of the social web and the relationship between Web 2.0 and the Semantic Web. It describes how blogs, wikis, and social networks enabled new forms of user-generated content and social interaction online in the early 2000s. The document also explains how Semantic Web technologies could enhance Web 2.0 by enabling the standardized exchange and combination of user data and services.
The document discusses predicting human behavior and privacy issues in online social networks. It covers topics like understanding human behavior in social communities, user data management and inference, enabling new human experiences through reality mining and context awareness, and privacy concerns in online social networks. Architectural frameworks and methodologies are presented for managing user data, generating new knowledge, and exposing services to predict behavior and enhance experiences while maintaining user privacy.
This document provides an overview of deep recommender systems and some of their shortcomings. It discusses neural network architectures like NeuMF, Wide&Deep, Neural FM, DeepFM, and DSCF that have been applied to recommendation. It also covers sequential recommendation methods, optimization techniques, and challenges like short-term rewards, manually designed architectures, isolated data, and security issues like poisoning attacks.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
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.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
This document provides an overview of social network analysis and visualization techniques. It discusses modeling and representing social networks as graphs. Key concepts in social network analysis like centrality, clustering, and path length are introduced. Visualization techniques for different types of online social networks like web communities, email groups, and digital libraries are surveyed. These include node-link diagrams, matrix representations, and hybrid approaches. Centrality measures like degree, betweenness, and closeness are also covered.
Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
The document discusses concepts in social network analysis including measuring networks through embedding measures and positions/roles of nodes. It covers network measures such as reciprocity, transitivity, clustering, density, and the E-I index. It also discusses positions like structural equivalence and regular equivalence and how to compute positional similarity through adjacency matrices.
Social Recommender Systems Tutorial - WWW 2011idoguy
The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
Recommendation Engines for Scientific LiteratureKris Jack
I gave this talk at the Workshop on Recommender Enginer@TUG (http://bit.ly/yuxrAM) on 2012/12/19.
It presents a selection of algorithms and experimental data that are commonly used in recommending scientific literature. Real-world results from Mendeley's article recommendation system are also presented.
The work presented here has been partially funded by the European Commission as part of the TEAM IAPP project (grant no. 251514) within the FP7 People Programme (Marie Curie).
The document discusses information retrieval (IR) and provides definitions and examples of different IR models and techniques. It describes how documents and queries can be represented as vectors, with weights like term frequency-inverse document frequency (tf-idf) used to indicate importance. Various IR models are covered, including boolean, vector space, and probabilistic models, along with common weighting and ranking methods used in IR systems.
This document discusses community detection in social media and online networks. It defines communities as groups of densely interconnected nodes in a graph. It outlines various algorithms for detecting communities, including graph partitioning, k-clique detection, core decomposition, divisive algorithms based on edge centrality, and modularity maximization approaches. It also discusses local community detection methods and evaluation of community detection results.
The document discusses social recommender systems and how they can improve on traditional collaborative filtering approaches by incorporating trust relationships between users. It outlines research that used trust propagation algorithms to make recommendations for cold start users who lack sufficient rating histories. The author proposes to further explore how different types of social relationships (e.g. trust, friendship) differentially impact recommendation performance and to evaluate social and similarity-based collaborative filtering approaches.
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Graph theory concepts like centrality, clustering, and node-edge diagrams are used to analyze social networks. Visualization techniques include matrix representations and node-link diagrams, each with advantages. Hybrid representations combine these to leverage their strengths. MatrixExplorer allows interactive exploration of social networks using both matrix and node-link views.
The document discusses the emergence of the social web and the relationship between Web 2.0 and the Semantic Web. It describes how blogs, wikis, and social networks enabled new forms of user-generated content and social interaction online in the early 2000s. The document also explains how Semantic Web technologies could enhance Web 2.0 by enabling the standardized exchange and combination of user data and services.
The document discusses predicting human behavior and privacy issues in online social networks. It covers topics like understanding human behavior in social communities, user data management and inference, enabling new human experiences through reality mining and context awareness, and privacy concerns in online social networks. Architectural frameworks and methodologies are presented for managing user data, generating new knowledge, and exposing services to predict behavior and enhance experiences while maintaining user privacy.
This document provides an overview of deep recommender systems and some of their shortcomings. It discusses neural network architectures like NeuMF, Wide&Deep, Neural FM, DeepFM, and DSCF that have been applied to recommendation. It also covers sequential recommendation methods, optimization techniques, and challenges like short-term rewards, manually designed architectures, isolated data, and security issues like poisoning attacks.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
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.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
This document provides an overview of social network analysis and visualization techniques. It discusses modeling and representing social networks as graphs. Key concepts in social network analysis like centrality, clustering, and path length are introduced. Visualization techniques for different types of online social networks like web communities, email groups, and digital libraries are surveyed. These include node-link diagrams, matrix representations, and hybrid approaches. Centrality measures like degree, betweenness, and closeness are also covered.
Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
The document discusses concepts in social network analysis including measuring networks through embedding measures and positions/roles of nodes. It covers network measures such as reciprocity, transitivity, clustering, density, and the E-I index. It also discusses positions like structural equivalence and regular equivalence and how to compute positional similarity through adjacency matrices.
Social Recommender Systems Tutorial - WWW 2011idoguy
The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
Recommendation Engines for Scientific LiteratureKris Jack
I gave this talk at the Workshop on Recommender Enginer@TUG (http://bit.ly/yuxrAM) on 2012/12/19.
It presents a selection of algorithms and experimental data that are commonly used in recommending scientific literature. Real-world results from Mendeley's article recommendation system are also presented.
The work presented here has been partially funded by the European Commission as part of the TEAM IAPP project (grant no. 251514) within the FP7 People Programme (Marie Curie).
The document discusses information retrieval (IR) and provides definitions and examples of different IR models and techniques. It describes how documents and queries can be represented as vectors, with weights like term frequency-inverse document frequency (tf-idf) used to indicate importance. Various IR models are covered, including boolean, vector space, and probabilistic models, along with common weighting and ranking methods used in IR systems.
Personalized Information Retrieval system using Computational Intelligence Te...veningstonk
The document presents research on developing a personalized information retrieval system using computational intelligence techniques. It discusses four proposed models: 1) a term association graph model for document re-ranking, 2) a topic model for document re-ranking, 3) a genetic intelligence model for document re-ranking, and 4) a swarm intelligence model for search query reformulation. The objectives are to improve retrieval effectiveness using term graphs and enhance personalized ranking using user topic modeling. Computational techniques like genetic algorithms and ant colony optimization will be used to re-rank documents and reformulate queries.
This document discusses music data mining. It provides an overview of music data mining tasks and approaches. Some key tasks discussed are music similarity search, clustering, and music sequence mining. Music similarity search involves finding music files similar to a given file based on features and similarity measures. Clustering divides music data into groups of similar objects based on pairwise similarity. Music sequence mining finds frequent patterns that appear in the ordered sequences of elements in music data instances.
Presenter: Dirk Arend, Senior IT Manager, Aperto
Providing visitors with a tailored experience is important and crucial for the success of many types of B2C and B2B sites. In this presentation, Dirk will show how Magnolia's personalization tools can be extended to dynamically deliver content to different visitor segments. Furthermore, he will showcase an integration of external data sources for rendering user specific variations of components. Using this method, Dirk will show it is not necessary to create variations of pages, thus streamlining content editing and management.
The document introduces different types of recommender systems including search-based recommendations, category-based recommendations, collaborative filtering, clustering, association rules, and information filtering. It discusses the key aspects of each approach such as how recommendations are generated, advantages, and limitations. The document also presents a taxonomy for classifying recommender systems based on factors like targeted customer inputs, community inputs, recommendation methods, and outputs.
This document discusses real-time recommendation systems and describes the Sifarish recommendation engine implementation. Sifarish uses Hadoop, Storm, and Redis to process both batch and real-time recommendations. It generates recommendations through content-based analysis, social recommendations based on user behavior, and real-time processing of new user event data through Storm. Sifarish provides features like implicit rating generation, item correlation analysis, time-sensitive recommendations, and business goal injection for generating personalized recommendations at scale.
Enhancing Information Retrieval by Personalization Techniquesveningstonk
This document outlines the research modules proposed for a PhD thesis focused on enhancing information retrieval through personalization techniques. The research will include four modules: 1) enhancing retrieval using term association graph representation, 2) integrating document and user topic models for personalization, 3) using genetic algorithms for document re-ranking, and 4) employing ant colony optimization for query reformulation. Module 1 will represent documents as a term graph and use the graph to re-rank documents based on term associations. The methodology for Module 1 includes preprocessing, frequent itemset mining to construct the term graph, and approaches for ranking documents based on semantic associations in the graph.
This document provides recommendations for using social media to create engagement for an International Centre. It recommends using blogs, Facebook, YouTube, Twitter and Pinterest to share content about international students, immigration, culture and events. Specific tactics include running contests for student blogs, sending invitations on Facebook, creating YouTube playlists on immigration/culture, and sending electronic birthday cards on Pinterest. The goal is to build relationships, connect with students, engage audiences and influence people through regular posting on each platform.
The document provides a media recommendation summary to launch a new Tacodeli location in San Francisco. It recommends a video seeding campaign on TV with a $90,000 budget to target 2.5 million impressions. It also recommends an out-of-home billboard campaign with a $65,000 budget to target 150,000 engagements. Additionally, it recommends social media engagement on Facebook, Twitter and Yelp with a total $36,000, $62,500 and $25,500 budgets respectively to generate over 40,000 clicks and engagements. The total proposed media budget is $500,000 to drive over 10 million impressions and engagements over the launch period from June to August 2016.
Qcon SF 2013 - Machine Learning & Recommender Systems @ Netflix ScaleXavier Amatriain
The document summarizes Netflix's approach to machine learning and recommender systems. It discusses how Netflix uses algorithms like SVD and Restricted Boltzmann Machines on a massive scale to power highly personalized recommendations. Over 75% of what people watch on Netflix comes from recommendations. Netflix collects a huge amount of data from over 40 million subscribers and uses both offline, online, and nearline computation across cloud services to train models and power recommendations in real-time at scale. The key is combining more data, smarter models, accurate metrics, and optimized system architectures.
Social media mining extracts information from social media sources like Facebook, Twitter, and YouTube to understand phenomena and improve services. It addresses challenges from vast, noisy, distributed, unstructured, and dynamic social media data. Common data mining tools and techniques are used to analyze social media data for applications like personalization, targeted marketing, community analysis, and sentiment analysis. Research issues include privacy and developing methods to effectively handle large-scale social media data.
Content Recommendation Based on Data Mining in Adaptive Social NetworksMarcel Caraciolo
The document discusses content recommendation in adaptive social networks based on data mining. It aims to design a methodology for social recommender systems that incorporate different knowledge sources from structured and unstructured data. The objectives are to design improved explanations for recommendations to increase user acceptance and enhance the student experience. The approach uses a hybrid recommender system that adapts the weighting of collaborative and content-based filtering based on the type of content being recommended. Current results show the system integrated into a Brazilian social network with over 70,000 students and items, with early user feedback being positive. Expected results include analyzing how recommendations can improve the learning process and exploring hidden knowledge in social networks.
Scott gilbertaccountplanningfruitpaletteAdManScott
The document summarizes research and planning for an advertising campaign for The Fruit Palette, a fruit-based restaurant. Research found that most people in the target area are unaware of The Fruit Palette. The target market identified is "Hustlers and Bustlers," busy professionals ages 18-44 who want a quick relaxing break. The brand destination is positioned as offering a "Vaca-From-My-Day" - a soothing, beach-like atmosphere with tropical treats allowing customers to relax and recharge for a few minutes. The creative work plan will develop advertising to raise awareness of The Fruit Palette as a unique escape for busy professionals needing a break.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Xavier Amatriain
The document summarizes a presentation on recommender systems given by Xavier Amatriain. It begins with introductions to recommender systems and collaborative filtering. Traditional collaborative filtering approaches include user-based and item-based methods. User-based CF finds similar users to a target user and recommends items they liked. Item-based CF finds similar items to those a target user liked and predicts ratings. Both approaches address sparsity and scalability challenges with dimensionality reduction techniques.
Recommender System _Module 1_Introduction to Recommender System.pptxSatyam Sharma
This document provides an introduction and overview of a module on recommender systems. The module aims to help students understand the importance and basic concepts of recommender systems. The syllabus covers introduction to recommender systems, different types of recommender systems including collaborative filtering, content-based, and knowledge-based systems. It also discusses hybrid systems, application and evaluation techniques, and emerging topics and challenges. The objective is for students to learn the basic concepts, understand different recommender system types, and be able to evaluate recommender systems as a multidisciplinary field.
This document discusses building an impersonal recommendation system using big data. It describes different recommendation approaches like collaborative filtering, knowledge-based, and content-based recommendations. An impersonal recommender provides suggestions without user profiles by analyzing customer purchase histories to find related item associations. The document proposes using Apache Hadoop to store and process large datasets for generating association rules to power recommendations. Elasticsearch would store and serve the rules to power an online recommender evaluation and improvement.
Web personalization involves customizing content for individual users based on their behavior and preferences. There are three main methods of personalization: implicit, which monitors user search history and details; explicit, which allows users to select their interests; and hybrid, which combines implicit and explicit. Personalization aims to provide relevant information to users without requiring explicit requests, using content-based filtering of user profiles or social/collaborative filtering based on interests of similar users. Combining social and content-based filtering with an item ontology can improve recommendations for sparse user data.
Customer to Customer recommendation systemsksaif95
The document discusses customer-to-customer (C2C) recommendation systems. It begins by describing different types of e-commerce models including business-to-business, business-to-consumer, and C2C. It then explains that C2C markets allow customers to interact and recommend products and services to one another. Recommendation systems use collaborative filtering, content-based filtering, and hybrid approaches to predict items a user may like. The document provides an example of how Facebook tracks user data for recommendations. It describes the need for a C2C recommendation system and surveys literature in the field. It outlines the system requirements, major recommendation approaches, advantages and disadvantages of each approach, and provides screenshots of an example C
Recommendations are everywhere : music, movies, books, social medias, e-commerce web sites… The Web is leaving the era of search and entering one of discovery. This quick introduction will help you to understand this vast topic and why you should use it.
Social Media Mining - Chapter 5 (Data Mining Essentials)SocialMediaMining
The document discusses data mining and social media mining. It introduces the concept of knowledge discovery in databases (KDD) as the process of extracting useful patterns from raw data. The KDD process involves collecting and preprocessing data, then applying data mining algorithms to extract patterns. Common data mining algorithms covered are decision trees, naive Bayes classification, and k-nearest neighbors. The document also discusses text representation using vector space models and TF-IDF weighting.
Agent technology for e commerce-recommendation systemsAravindharamanan S
This document discusses recommender systems which provide recommendations to users based on their preferences and the preferences of similar users. It describes two main techniques for recommender systems: content-based filtering which provides recommendations based on a user's profile of interests, and collaborative filtering which identifies other users with similar tastes and provides recommendations based on their preferences. The document also discusses issues with recommender systems like data sparsity, cold start problems, and privacy concerns when user profiles are collected.
This document discusses how social media can be used for consumer insights in marketing research. It describes what types of data can be monitored on social media, including conversations, photos, videos and more. There are two main types of marketing research using social media: primary research involving direct data collection, and secondary research using existing internal or public data. Qualitative and quantitative research methods for analyzing social media data are also outlined. The document cautions about potential errors and biases when conducting social media research.
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
Introduction to Recommendation Systems (Vietnam Web Submit)Trieu Nguyen
1) Why do we need recommendation systems ?
2) How can we think with recommendation systems ?
3) How can we implement a recommendation system with open source technologies ?
RFX framework https://github.com/rfxlab
Apache Kafka: https://kafka.apache.org
Apache Spark: https://spark.apache.org
recommendation system techunique and issueNutanBhor
This document discusses recommendation system techniques and issues. It covers common recommendation approaches like content-based filtering, collaborative filtering, and hybrid systems. It also addresses challenges like cold start problems, privacy issues, and data sparsity. Recommendation systems analyze user preferences to suggest new items, and are used by applications like ecommerce sites, streaming services, and social networks to provide personalized recommendations. While useful, they also present technical challenges for researchers.
The document summarizes a master's thesis presentation on developing a recommender system called SocioNet-Receiver. SocioNet-Receiver aims to provide serendipitous recommendations from a user's social network by analyzing aspects like the user profile, relationships, locations and times. It extracts potential recommendations, scores them based on how serendipitous and accurate they are, and provides a final recommendation. The goal is to surface interesting and unexpected stories for users within their limited time on an infotainment system.
IRJET- Hybrid Book Recommendation SystemIRJET Journal
This document describes a hybrid book recommendation system that aims to overcome some common issues with recommendation systems like the cold start problem. The system collects demographic information from users during signup to provide more personalized recommendations. It uses both collaborative and content-based filtering approaches. For new users, it recommends books based on their interests. For users without ratings, it considers their purchase history. For users who provide ratings, it uses algorithms like KNN, SVD, RBM and hybrid approaches. The system aims to improve accuracy and provide a more personalized experience for users.
This document summarizes techniques for establishing trust in recommender systems. It discusses aspects of trust like social awareness, robustness, and explainability. It then outlines different recommendation methods like collaborative filtering, autoencoders, RNNs, and GNNs that leverage social behaviors and graphs. It also discusses making systems robust against shilling attacks and developing explainable recommender systems that help users understand recommendations through text, visuals, or multimodal explanations. The conclusion states that as recommendation systems become more advanced and prevalent, establishing trust will become increasingly important.
Movie recommendation system using collaborative filtering system Mauryasuraj98
The document describes a mini project on building a movie recommendation system. It includes an abstract that discusses different recommendation approaches like demographic, content-based, and collaborative filtering. It also outlines the problem statement, proposed solution, workflow, dataset description, algorithm details, GUI design, result analysis, and applications. The system uses a user-based collaborative filtering model to recommend movies to users based on their preferences and ratings of similar users. Evaluation shows it has good prediction performance.
Use of data science in recommendation systemAkashPatil334
This document discusses the use of data science in recommendation systems. It defines recommendation systems as systems that predict a user's preferences for items and recommend top items. It also defines data science as using scientific methods to extract knowledge from structured and unstructured data. The document then describes different types of recommendation systems, including collaborative filtering, content-based filtering, and hybrid systems. It provides examples of how Netflix, Amazon, LinkedIn, and Pandora use recommendation systems.
Similar to Social Media Mining - Chapter 9 (Recommendation in Social Media) (20)
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
2. 2Social Media Mining Measures and Metrics 2Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Dear instructors/users of these slides:
Please feel free to include these slides in your own
material, or modify them as you see fit. If you decide
to incorporate these slides into your presentations,
please include the following note:
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining:
An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
or include a link to the website:
http://socialmediamining.info/
3. 3Social Media Mining Measures and Metrics 3Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Difficulties of Decision Making
• Which digital camera should I buy?
• Where should I spend my holiday?
• Which movie should I rent?
• Whom should I follow?
• Where should I find interesting news article?
• Which movie is the best for our family?
• If interested, see two recent conference tutorials
– SIGKDD2014, Recommendation in Social Media
– RecSys2014, Personalized Location Recommendation
4. 4Social Media Mining Measures and Metrics 4Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
When Does This Problem Occur?
• There are many choices
• There are no obvious advantages among them
• We do not have enough resources to check all
options (information overload)
• We do not have enough knowledge and
experience to choose, or
– I’m lazy, but don’t want to miss out on good stuff
– Defensive decision making
Goal of Recommendation:
To come up with a short list of
items that fits user’s interests
5. 5Social Media Mining Measures and Metrics 5Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Common Solutions to the Problem
• Consulting friends
• Obtaining information from a trusted third party
• Hiring a team of experts
• Search the Internet
• Following the crowd
– Pick the item from top-𝑛 lists
– Best sellers on Amazon
• Can we automate all of the above?
– Using a recommender algorithm
– Also known as recommender systems
6. 6Social Media Mining Measures and Metrics 6Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Recommender Systems - Examples
7. 7Social Media Mining Measures and Metrics 7Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Main Idea behind Recommender Systems
• Users’ preferences are likely to remain stable,
and change smoothly over time.
– By watching the past users’ or groups’ preferences,
we try to predict their future interests
– Then we can recommend items of interest to them
• Formally, a recommender system takes a set
of users 𝑈 and a set of items 𝐼 and learns a
function 𝑓 such that:
Use historical data such as the user’s past preferences
or similar users’ past preferences to predict future likes
8. 8Social Media Mining Measures and Metrics 8Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Recommendation vs. Search
• One way to get answers is using search engines
• Search engines find results that match the query
provided by the user
• The results are generally provided as a list
ordered with respect to the relevance of the item
to the given query
• Consider the query “best 2014 movie to watch”
– The same results for an 8 year old and an adult
Search engines’ results are not customized
9. 9Social Media Mining Measures and Metrics 9Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Challenges of Recommender Systems
• The Cold Start Problem
– Recommender systems use historical data or
information provided by the user to recommend
items, products, etc.
– When user join sites, they still haven’t bought any
product, or they have no history.
– It is hard to infer what they are going to like when
they start on a site.
• Data Sparsity
– When historical or prior information is insufficient.
– Unlike the cold start problem, this is in the system as
a whole and is not specific to an individual.
10. 10Social Media Mining Measures and Metrics 10Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Challenges of Recommender Systems
• Attacks
– Push Attack: pushing ratings up by making fake users
– Nuke attack: DDoS attacks, stop the whole
recommendation systems
• Privacy
– Using one’s private info to recommend to others.
• Explanation
– Recommender systems often recommend items with
no explanation on why these items are recommended
11. 11Social Media Mining Measures and Metrics 11Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
• Content-based algorithms
• Collaborative filtering
Classical
Recommendation Algorithms
12. 12Social Media Mining Measures and Metrics 12Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Content-Based Methods
Assumption: a user’s interest should match the
description of the items that the user should be
recommended by the system.
– The more similar the item’s description to that of the
user’s interest, the more likely that the user finds the
item’s recommendation interesting.
Goal: find the similarity between the user
and all of the existing items is the core of
this type of recommender systems
13. 13Social Media Mining Measures and Metrics 13Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Content-based Recommendation: An Example
BookDatabase
User
Profile
14. 14Social Media Mining Measures and Metrics 14Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Content-based Recommendation Algorithm
1. Describe the items to be recommended
2. Create a profile of the user that describes the
types of items the user likes
3. Compare items with the user profile to
determine what to recommend
The profile is often created, and updated
automatically in response to feedback on the
desirability of items that are presented to the user
15. 15Social Media Mining Measures and Metrics 15Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Content-based Recommendation: Example
Items Recommended
User Profile
16. 16Social Media Mining Measures and Metrics 16Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
More formally
• We represent user profiles and item descriptions
by vectorizing them using a set of 𝑘 keywords
• We can vectorize (e.g., using TF-IDF) both users
and items and compute their similarity
We can recommend the top most
similar items to the user
17. 17Social Media Mining Measures and Metrics 17Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Content-Based Recommendation Algorithm
• We compute the topmost similar items to a
user 𝑗 and then recommend these items in the
order of similarity
18. 18Social Media Mining Measures and Metrics 18Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Collaborative Filtering
Collaborative filtering: the process of selecting
information or patterns using techniques involving
collaboration among multiple agents, viewpoints,
data sources, etc.
Advantage: we don’t need to have additional
information about the users or content of the items
– Users’ rating or purchase history is the only information
that is needed to work
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Rating Matrix: An Example
20. 20Social Media Mining Measures and Metrics 20Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Rating Matrix
Users rate (rank) items (purchased, watched)
Explicit ratings:
– entered by a user directly
– i.e., “Please rate this on a scale of 1-5”
Implicit ratings:
– Inferred from other user behavior
– E.g., Play lists or music listened to, for a music Rec system
– The amount of time users spent on a webpage
21. 21Social Media Mining Measures and Metrics 21Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Collaborative Filtering
Types of Collaborative Filtering Algorithms:
• Memory-based: Recommendation is directly
based on previous ratings in the stored matrix
that describes user-item relations
• Model-based: Assumes that an underlying model
(hypothesis) governs how users rate items.
– This model can be approximated and learned.
– The model is then used to recommend ratings.
– Example: users rate low budget movies poorly
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Memory-Based Collaborative Filtering
Two memory-based methods:
User-based CF
Users with similar previous
ratings for items are likely to rate
future items similarly
Item-based CF
Items that have received similar
ratings previously from users are
likely to receive similar ratings
from future users
23. 23Social Media Mining Measures and Metrics 23Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Collaborative Filtering: Algorithm
1. Weigh all users/items with respect to their
similarity with the current user/item
2. Select a subset of the users/items (neighbors) as
recommenders
3. Predict the rating of the user for specific items
using neighbors’ ratings for the same (or similar)
items
4. Recommend items with the highest predicted rank
24. 24Social Media Mining Measures and Metrics 24Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Measuring Similarity between Users (or Items)
Cosine Similarity
Pearson Correlation Coefficient
25. 25Social Media Mining Measures and Metrics 25Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
User-based Collaborative Filtering
• User-based collaborative filtering
– The system finds the most similar user (users) to
the current user and uses their preferences for
recommendation
• The user-based approach is not as
popular as the item-based approach
– Why? With large number of users, even the
smallest change in the user data is likely to
reset the entire group of similar users
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User-based CF
Updating the ratings:
Predicted rating of
user 𝑢 for item 𝑖
User 𝑢‘s mean
rating
Observed rating of
user 𝑣 for item 𝑖
User 𝑣’s mean rating
27. 27Social Media Mining Measures and Metrics 27Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
User-based CF, Example
Predict Jane’s rating
for Aladdin
1- Calculate average ratings 2- Calculate user-user similarity
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User-based CF, Example- continued
3- Calculate Jane’s rating for Aladdin, Assume that neighborhood size = 2
29. 29Social Media Mining Measures and Metrics 29Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Item-based CF
Calculate the similarity between items and then
predict new items based on the past ratings for
similar items
𝑖 and 𝑗 are two itemsItem 𝑖’s mean rating
30. 30Social Media Mining Measures and Metrics 30Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Item-based CF, Example
1- Calculate average ratings 2- Calculate item-item similarity
3- Calculate Jane’s rating for Aladdin, Assume that neighborhood size = 2
31. 31Social Media Mining Measures and Metrics 31Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Model-Based Collaborative Filtering
• In memory-based methods
– We predict the missing ratings based on similarities
between users or items.
• In model-based collaborative filtering
– We assume that an underlying model governs how
users rate.
• We learn that model and use it to predict the
missing ratings.
– Among a variety of model-based techniques, we focus
on a well-established model-based technique that is
based on singular value decomposition (SVD).
32. 32Social Media Mining Measures and Metrics 32Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Singular Value Decomposition (SVD)
• SVD is a linear algebra technique that, given a
real matrix 𝑋 ∈ ℝ 𝑚×𝑛, 𝑚 ≥ 𝑛, and factorizes it
into three matrices
• Matrices U ∈ ℝ 𝑚×𝑚 and 𝑉 ∈ ℝ 𝑛×𝑛 are
orthogonal and matrix Σ ∈ ℝ 𝑚×𝑛
is diagonal
• The product of these matrices is equivalent to
the original matrix
– No information is lost!
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Low-rank Matrix Approximation
• A Low-rank matrix approximation of matrix X ∈ ℝ 𝑚×𝑛 is another
matrix 𝐶 ∈ ℝ 𝑚×𝑛
• Matrix 𝐶 approximates 𝑋, and 𝐶’s rank (the maximum number of
linearly independent columns) is a fixed number 𝑘 ≪ min(𝑚, 𝑛)
𝑅𝑎𝑛𝑘(𝐶) = 𝑘
• The best low-rank matrix approximation is a matrix C that
minimizes ||𝑋 − 𝐶|| 𝐹
• Low-rank approximation can remove noise by assuming that the
matrix is not random and has an underlying structure.
– SVD can compute a low-rank approximation of a matrix.
34. 34Social Media Mining Measures and Metrics 34Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Low-Rank Matrix Approximation with SVD
𝑿 𝒌 is the best low-rank approximation of a matrix 𝑿
35. 35Social Media Mining Measures and Metrics 35Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Model-based CF, Example
Considering a rank 2 approximation (i.e., k = 2), we truncate all three matrices:
36. 36Social Media Mining Measures and Metrics 36Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Recommendation to
a Group
37. 37Social Media Mining Measures and Metrics 37Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Recommendation to Groups
• Find content of interest to all members of a
group of socially acquainted individuals
• Examples:
– A movie for friends to watch together
– A travel destination for a family to spend a break
– A good restaurant for colleagues to have lunch
– A music to be played in a public area
38. 38Social Media Mining Measures and Metrics 38Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Tasks of a Group Recommender System
• Acquiring preferences
• Generating recommendations
• Explaining recommendations
• Helping group members to achieve consensus
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Aggregation Strategies
Maximizing Average Satisfaction
– Average everyone’s ratings and
choose the max
Least Misery
– This approach tries to minimize the
dissatisfaction among group’s
members (max of all mins)
Most Pleasure
– The maximum of individuals’
maximum ratings is taken as
group’s rating
40. 40Social Media Mining Measures and Metrics 40Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Recommendation to Group, an Example
Average Satisfaction Least Misery Most Pleasure
41. 41Social Media Mining Measures and Metrics 41Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
• Recommendation using social context alone
• Extending classical methods with social context
• Recommendation constrained by social context
Recommendation Using
Social Context
42. 42Social Media Mining Measures and Metrics 42Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Information Available in Social Context
• In social media, in addition to ratings of products,
there is additional information
– E.g., the friendship network
• This information can be used to improve
recommendations
• Assuming that friends have an
impact on the ratings ascribed by
the individual.
• This impact can be due to
homophily, influence, or
confounding
43. 43Social Media Mining Measures and Metrics 43Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
I. Recommendation Using Social Context Alone
• Consider a network of friendships for which
no user-item rating matrix is provided.
• In this network, we can still recommend users
from the network to other users for
friendship.
• This is an example of friend recommendation
in social networks [Next Chapter!]
44. 44Social Media Mining Measures and Metrics 44Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
II. Extending Classical Methods
• Using Social information in addition to a user-item
rating matrix to improve recommendation.
• Addition of social information:
– We assume that friends rate similar items similarly.
Optimize only for non-zero elements
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Modeling Social Information in Recommendation
• Incorporating similarity: The taste for user 𝑖
is close to that of all his friends 𝑗 ∈ 𝐹(𝑖)
– 𝑠𝑖𝑚(𝑖, 𝑗) denotes the similarity between user 𝑖 and 𝑗
(e.g., cosine between their ratings)
– 𝐹(𝑖) denotes the friends of 𝑖
• Final Formulation:
Controlling Sparsity
46. 46Social Media Mining Measures and Metrics 46Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
3. Recommendation Constrained by Social Context
• In classical recommendation,
– To estimate ratings, we determine similar users or items.
– Any user similar to the individual can contribute to the
predicted ratings for the individual.
• We can limit the set of individuals that can
contribute to the ratings of a user to the set of
friends of the user.
– 𝑆(𝑖) is the set of 𝑘 most similar friends of an individual
47. 47Social Media Mining Measures and Metrics 47Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
ExampleAverage
RatingsUserSimilarity
48. 48Social Media Mining Measures and Metrics 48Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Evaluation
of
Recommender Systems
49. 49Social Media Mining Measures and Metrics 49Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Evaluating Recommender Systems is difficult
• Different algorithms may be better or worse on
different datasets (applications)
– Many algorithms are designed specifically for datasets where
there are many more users than items or vice versa.
– Similar differences exist for rating density, rating scale, and
other properties of datasets
• The goals to perform evaluation may differ
– Early evaluation work focused specifically on the "accuracy" of
algorithms in "predicting" withheld ratings.
– Other properties different from accuracy also have important
effect on user satisfaction and performance
• There is a significant challenge in deciding what
combination of measures should be used in
comparative evaluation
50. 50Social Media Mining Measures and Metrics 50Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Evaluating Recommender Systems
• A myriad of algorithms are proposed, but
– Which one is the best in a given application domain?
– What are the success factors of different algorithms?
– Comparative analysis based on an optimality criterion?
Main questions are:
– Is a RS efficient with respect to specific criteria like
accuracy, user satisfaction, response time, etc.
– Do customers like/buy recommended items?
– Do customers buy items they otherwise would have not?
– Are they satisfied with a recommendation after purchase?
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How Do We Evaluate Recommenders
• Application outcomes
– Add-on sales
– Click-through rates
– The number of products purchased
• And not returned!
• Research measures
– User satisfaction
• Metrics
– To anticipate the above beforehand (offline)
52. 52Social Media Mining Measures and Metrics 52Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Accuracy Metrics
• Predictive accuracy
– How close are the recommender system’s predicted
ratings are to the true user ratings?
• Classification accuracy
– The ratio with which a recommender system
makes correct vs. incorrect decisions about
whether an item is good.
– Classification metrics are thus appropriate for
tasks such as Find Good Items when users have
binary preferences.
• Rank accuracy
53. 53Social Media Mining Measures and Metrics 53Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
I. Predictive accuracy - Metrics measure error rate
• Mean Absolute Error (MAE).
The average absolute deviation
between a predicted rating (𝑝)
and the user’s true rating (𝑟)
– 𝑁𝑀𝐴𝐸 = 𝑀𝐴𝐸/(𝑟 𝑚𝑎𝑥 – 𝑟 𝑚𝑖𝑛)
• Root Mean Square Error
(RMSE). Similar to MAE, but
places more emphasis on larger
deviation
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Evaluation Example
55. 55Social Media Mining Measures and Metrics 55Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
II. Classification Accuracy: Precision and Recall
Precision: a measure of exactness,
determines the fraction of relevant
items retrieved out of all items retrieved
Recall: a measure of completeness,
determines the fraction of relevant
items retrieved out of all relevant items
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Evaluating Relevancy, Example
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III. Evaluating Ranking of Recommendation
• Spearman’s Rank Correlation
𝜌 = 1 −
6 𝑖=1
𝑛
𝑥𝑖 − 𝑦𝑖 2
𝑛3 − 𝑛
• Kendall’s 𝝉
– Compares concordant the items of the recommended
ranking list against the ground truth ranking list
• If the two orders are consistent, it is concordant
• E.g., for top 4 items in ranking list, there are 4×3/2=6 pairs
𝜏 =
𝑐−𝑑
𝑛
2
• 𝑐 is the number of concordants
• 𝑑 is the number of disconcordants
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Ranking, Example
Consider a set of four items 𝐼 = {𝑖1 , 𝑖2 , 𝑖3 , 𝑖4} for which the predicted and true
rankings are as follows
Pair of items and their status
{concordant/discordant} are
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Extra Slides
60. 60Social Media Mining Measures and Metrics 60Social Media Mining Recommendation in Social Mediahttp://socialmediamining.info/
Beyond Accuracy, Relevance, and Rank
• Coverage
– Measure of the domain of items in the system over which the
system can form predictions or make recommendations
• Novelty and Serendipity
– Helping users to find a surprisingly interesting item he might
not have otherwise discovered
• Confidence
– How sure is the RS that its recommendation is accurate?
• Diversity
• Risk
• Robustness
• Privacy
• Adaptivity
• Scalability
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Content Recommendation in
Social Media
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Video Recommendation
• Related videos defined as videos that a user is
likely to watch after having watched a video
• Approaches to video recommendation:
– Association rule mining
– co-visitation
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Video Recommendation: Association Rule Mining
System calculates the probability of watching vj
after the user watched vi and recommends top-
N of highly ranked videos
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Video Recommendation: Co-visitation
Co-visitation score is a number that shows in a
given time period, how often two videos co-
watched within sessions
cij is the number of co-watches for videos vi and vj and f
(vi, vj) is a normalization factor regarding the popularity of
two videos, e.g., e the product of the videos’ popularity
(the number of views).
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Tag Recommendation
• Tag recommendation is the process of
recommending appropriate tags to be applied
by the user per specific item annotation
• Approaches:
– Recommending the most popular tags
– Collaborative Filtering
– Content-based Tag Recommendation
– Graph-based
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Tag Recommendation: Popularity-based and CF
Approaches
Recommend the most popular tags
– Popular tags already assigned for the target item
– Frequent tags previously used by the user, and
– Tags co-occurred with already assigned tags.
Collaborative filtering
– It can use item-based or user-based approaches.
– Or it may use a hybrid approach by
recommending tags given by similar users to
similar items.
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Tag Recommendation: Content- and Graph-based
Approaches
Content-based Tag Recommendation
– This can be done by recommending keywords
from the item’s associated text or tags that have
the highest co-occurrence with important
keywords.
Graph-based Approaches
– The FolkRank algorithm is an example. Its idea is:
a resource which is tagged with tags by important
users should be important.
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News Recommendation
• A recommended news should be of interest to
the user if it is recent or fresh, diverse, and
not very similar (the same) to the other news
the user recently read.
• Regular recommender systems might not be
used for news as recency is one of the most
important factors for a piece of news.
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Blog Recommendation
• The blog recommendation is the task of
finding relevant blogs in response to a query
• Blog relevance ranking differs from classical
document retrieval and ranking in several
ways:
– How to deal with blog posts
– How to come up with reliable queries as user
queries represent current interests in the topic
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Blog Recommendation: An Algorithm
• A solution for the first problem is using two
different document models
– Large document model: entire blog as a whole
– Small document model: each blog post as a document.
• The second problem can be solved by query
expansion
– Query Expansion (QE) is the process of reformulating a
seed query to improve retrieval performance in
information retrieval operations).
• Using wikipedia is an often used method for query
expansion: the query is sent to Wikipedia, and the retrieved
wiki articles are now new queries and will be used for blog
searching.
• After solving these two problems of blogs, we can
use existing recommendation systems to get the
blog recommendation.
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Social Media Content Recommendation: Tag
Based
• People use tags to summarize, remember, and
organize information.
• Tags are a powerful tool for social navigation,
helping people to share and discover new
information contributed by other community
members.
• Tags promote social navigation by their
vocabulary, or the set of tags used by members of
the community.
• Instead of imposing controlled vocabulary or
categories, tagging systems’ vocabulary emerges
organically from the tags chosen or created by
individual members.
• A tag-based recommendation system uses tags to
recommend items.
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Tag Based Recommendation
• Algorithms combining tags with
recommenders provide both the automation
inherent in recommenders, and the flexibility
and conceptual comprehensibility inherent in
the tagging system.
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Hybrid Approaches to
Recommendation
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Pipeline hybridization
• This approach uses more than one
recommender system and puts the
recommender systems in a line.
• The result of one recommender is the input
for another one.
• The earlier recommender can make a model
of the input and pass it to the next
recommender system or can generate a list of
recommendations to be used by the next
recommender system.
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Parallelized hybridization
• We use more than one recommender
algorithm.
• The hybridization process gets the result of
recommenders, combines them and
generates the final recommendation.
• Different methods can be used to combine
the results of other recommenders such as:
– Mixed (a union of results from all recommender
systems),
– Weighted (a weighted combination of the results),
– Switching (use results from specific recommender
systems for specific tasks),
– Majority voting.
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The digital camera X is the
best for you because …
Recommendation
Explanation
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Confidence
• Why would someone distrust a
recommendation?
– Can I trust the provider?
– How does this work, anyway?
– Does the system know enough about me?
– Does the system know enough about the item it is
recommending?
– How sure is it?
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The Confidence Challenge
• Why should users believe recommendations?
• When should users believe them?
• Approaches
– Confidence indicators
– Explain the recommendations
• Reveal data and processes
• Corroborating data, track records
– Offer opportunities to correct mistaken data
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Objectives of explanations
• Transparency
– Provide information such that the user can comprehend the
reasoning used to generate a specific recommendation
• Validity
– Explanations can be generated in order to allow a user to check
the validity of a recommendation
• Trustworthiness
– Explanations aiming to build trust in recommendations reduce
the uncertainty about the quality of a recommendation
• Comprehensibility
– Explanations targeting comprehension support the user by
relating her known concepts to the concepts employed by the
recommender
• Education
– Deep knowledge about the domain helps the customer rethink
her preferences and evaluate the pros and cons of different
solutions
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Objectives of explanations
• Persuasiveness
– In this sense persuasive explanations for recommendations
aim to change the user's buying behavior
• Effectiveness
– The support a user receives for making high-quality decisions
• Efficiency
– A system's ability to support users in order to reduce the
decision-making effort e.g. time
• Satisfaction
– Explanations can attempt to improve the overall satisfaction
stemming from the use of a recommender system.
• Relevance
– Additional information may be required in conversational
recommenders. Explanations can be provided to justify why
additional information is needed from the user
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Examples
• Similarity between items
• Similarity between users
• Tags
– Tag relevance (for items)
– Tag preference (of users)
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Explanation types
• Nearest neighbor explanation
– Customers who bought item X also bought items Y,Z
– Item Y is recommended because you rated related item
X
• Content based explanation
– This story deals with topics X,Y which belong to your
topic of interest
• Social-network based explanation
– People leverage their social network to reach
information and make use of trust relationships to filter
information.
• Your friend X wrote that blog
• 50% of your friends liked this item (while only 5% disliked it)
Left panel is user profile (or model), right panel is the recommendation result used the user profile.
Can we use Jaccard index J(A,B) = |A^B|/|AvB|
Predict Jane’s rating for Aladdin
After most similar items or users are found in the lower k-dimensional space, one can follow the same process outlined in user-based or item-based collaborative filtering to find the ratings for an unknown item.
For instance, we showed in Example 9.3 (see Figure 9.1) that if we are predicting the rating rJill,Lion King and assume that neighborhood size is 1, item-based CF uses rJill,Aladdin , because Aladdin is closest to Lion King. Similarly, user-based collaborative filtering uses rJohn,Lion King , because John is the closest user to Jill.
Non-Preference Aggregation (See the four methods in slide): Max(averages of all), Max(least’s of all), Max(max’s of all)
Preference Aggregation
Merging of recommendations made for individuals
Aggregation of ratings for individuals
Construction of group preference models
Average Satisfaction
Recommendation using Social Context. When utilizing social information, we can 1) utilize this information independently, 2) add it to user-rating matrix, or 3) constrain recommendations with it.
R: Rating matrix,
U: New representation for users
Lambda_1 and Lambda_2 are predefined non-negative constants
where β is the constant that controls the effect of social network regularization.
A local minimum for this optimization problem can be obtained using gradient-descent-based approaches. To solve this problem, we can compute the gradient with respect to Ui’s and Vi’s and perform a gradient descent-based method.