Content tagging and recommender systems
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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.
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recommender.ir at a glance
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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.
243
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
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experiments have been promising
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context and activities of a user, recommends resources in an academic networking portal. The
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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.
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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
RUNNING HEADER Analytics Ecosystem1Analytics Ecosystem4.docx
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
An improved technique for ranking semantic associationst07
The primary focus of the search techniques in the first generation of the Web is accessing relevant
documents from the Web. Though it satisfies user requirements, but it is insufficient as the user sometimes
wishes to access actionable information involvin
g complex relationships between two given entities.
Finding such complex relationships (also known as semantic associations) is especially useful in
applications
such as
National Security, Pharmacy, Business Intelligence etc. Therefore the next frontier is
discovering relevant semantic associations between two entities present in large semantic metadata
repositories. Given two entities, there exist a huge number of semantic associations between two entities.
Hence ranking of these associations is required i
n order to find more relevant associations. For this
Aleman Meza et al. proposed a method involving six metrics viz. context, subsumption, rarity, popularity,
association length and trust. To compute the overall rank of the associations this method compute
s
context, subsumption, rarity and popularity values for each component of the association and for all the
associations. However it is obvious that, many components appears repeatedly in many associations
therefore it is not necessary to compute context, s
ubsumption, rarity
,
popularity
,
and
trust
values of the
components every time for each association rather the previously computed values may be used while
computing the overall rank of the associations. Thi
s paper proposes a method to re
use the previously
computed values using a hash data structure thus reduce the execution time. To demonstrate the
effectiveness of the proposed method, experiments were conducted on SWETO ontology. Results show
that the proposed method is more efficient than the other existi
ng methods
.
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Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
Framework for Product Recommandation for Review Dataset
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
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Building Production Ready Search Pipelines with Spark and Milvus
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
Content tagging and recommender systems
- 1. Capstone Report Presentation User-Generated Metadata in Social Software: An Analysis of Findability in Content Tagging and Recommender Systems Presented by Michael Barnes In partial fulfillment of the requirement for the degree of Master of Science. March, 2007
- 7. TIME-liness of Topic Since then, the rise of social software designed to leverage user-generated metadata has boomed. The 2006 Time Magazine’s “Person of the Year” was - the user! The rapid increase of user participation and user generated content in social software systems continues to gain popularity under the moniker of “Web 2.0.”
- 8. TIME-liness of Topic “ It's a story about community and collaboration on a scale never seen before. It's about the many wresting power from the few and helping one another for nothing and how that will not only change the world, but also change the way the world changes. It's a tool for bringing together the small contributions of millions of people and making them matter. And for seizing the reins of the global media, for founding and framing the new digital democracy, for working for nothing and beating the pros at their own game, TIME's Person of the Year for 2006 is you.”