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Recommender Systems.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2014Copyright date: ©2014Edition: 1st edDescription: 1 online resource (253 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119054245
Subject(s): Genre/Form: Additional physical formats: Print version:: Recommender SystemsLOC classification:
  • QA76.9.I58 -- .R43 2014eb
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface: Recommender Engines (and Systems) -- Acknowledgments -- Bibliography -- 1: General Introduction to Recommender Systems -- 1.1. Putting it into perspective -- 1.2. An interdisciplinary subject -- 1.3. The fundamentals of algorithms -- 1.3.1. Collaborative filtering -- 1.3.1.1. Advantages and drawbacks of collaborative filtering -- 1.3.2. Content filtering -- 1.3.2.1. Advantages and drawbacks of content filtering -- 1.3.3. Hybrid methods -- 1.3.4. Conclusion on historical recommendation models -- 1.4. Content offers and recommender systems -- 1.4.1. Culture and recommender systems -- 1.4.1.1. Recommendation and cinema -- 1.4.1.2. Recommendation and literature -- 1.4.1.3. Recommendation and general culture -- 1.4.2. Recommender systems and the e-commerce of content -- 1.4.3. The behavior of users -- 1.5. Current issues -- 1.6. Bibliography -- 2: Understanding Users' Expectations for Recommender Systems: the Case of Social Media -- 2.1. Introduction: the omnipresence of recommender systems -- 2.2. The social approach to prescription -- 2.2.1. The theory of the prescription and online interactions -- 2.2.2. Conditions for recognition of the prescription -- 2.2.3. The specificities of social media -- 2.3. Users who do not focus on the prescriptions of platforms -- 2.3.1. Facebook: the link, the type of activity and the context -- 2.3.2. Twitter: prescription between peers and explanation of prescription -- 2.3.3. Conditions for the recognition of a prescription: announcement and enunciation -- 2.4. A guide for considering recommender systems adapted to different forms of social media -- 2.5. Conclusion -- 2.6. Bibliography -- 3: Recommender Systems and Social Networks: What Are the Implications for Digital Marketing? -- 3.1. Social recommendations: an ancient practice revived by the digital age.
3.1.1. Recommendations: a difficult management for brands -- 3.1.2. Internet recommendations: social presence and personalized recommendations -- 3.2. Social recommendations: how are they used for e-commerce? -- 3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites -- 3.2.2. Recommender systems used by social networks: from e-commerce to social commerce -- 3.2.2.1. Facebook, innovator in its vision for social recommendation: Like, Edge Rank, Place, Social and Open Graph -- 3.2.2.2. Social recommendation, the cornerstone of an emerging social commerce -- 3.3. Conclusion -- 3.4. Bibliography -- 4: Recommender Systems and Diversity: Taking Advantage of the Long Tail and the Diversity of Recommendation Lists -- 4.1. The stakes associated with diversity within recommender systems -- 4.1.1. Individual diversity or the individual perception of diversity -- 4.1.2. The stakes and impacts of aggregate diversity -- 4.1.2.1. Markets with limited resources -- 4.1.2.2. Cultural diversity -- 4.1.2.3. The long-tail economy: toward a more diverse consumption -- 4.2. Recommendation algorithms and diversity: trends, evaluation and optimization -- 4.2.1. The tendency for recommendation algorithms to focus on the head -- 4.2.2. The evaluation of diversity in recommender systems -- 4.2.3. Recommendation algorithms which favor individual diversity -- 4.2.4. Recommendation algorithms which favor aggregate diversity -- 4.2.5. The shift toward user-centered diversity approaches -- 4.2.5.1. The perception of diversity by users -- 4.2.5.2. Analyzing users in order to improve aggregate diversity -- 4.3. Conclusion and new directions -- 4.4. Bibliography -- 5: iSoNTRE: Intelligent Transformer of Social Networks into a Recommendation Engine Environment -- 5.1. Summary -- 5.2. Introduction -- 5.3. Latest developments, definition and history.
5.3.1. Collaborative filtering techniques -- 5.3.2. General use social networks: what do they contain? -- 5.3.3. Social recommendation -- 5.3.4. The recommendation of concepts -- 5.4. iSoNTRE -- 5.4.1. iSoNTRE: transformer of social networks -- 5.4.1.1. Extracting concepts from profiles -- 5.4.1.1.1. Disambiguation -- 5.4.1.1.2. World sources of knowledge -- 5.4.1.2. Adding friends to the N-Facet model -- 5.4.2. iSoNTRE: the core of recommendation -- 5.4.2.1. Constructing the general matrix and the matrices specific to domains -- 5.4.2.2. Autonomous recommendation -- 5.4.2.3. Combined recommender systems -- 5.5. Experiments -- 5.5.1. The preparation of data -- 5.5.2. Testing methodology -- 5.5.3. The creation of avatars -- 5.5.4. Results -- 5.5.5. Discussion -- 5.6. Conclusion -- 5.7. Bibliography -- 6: A Two-Level Recommendation Approach for Document Search -- 6.1. Introduction -- 6.2. Tag recommendation: a brief state of the art -- 6.3. The hypertagging system -- 6.3.1. Metadata -- 6.3.2. Architecture -- 6.4. Recommendation approach -- 6.4.1. Presentation -- 6.4.1.1. Correlation between facets -- 6.4.1.2. Correlation between tags -- 6.4.2. Recommendation algorithm -- 6.5. Evaluation -- 6.5.1. Generation of facets -- 6.5.2. Generation of association rules -- 6.5.3. Evaluation of recommendation rules -- 6.6. Conclusion -- 6.7. Bibliography -- 7: Combining Configuration and Recommendation to Enable an Interactive Guidance of Product Line Configuration -- 7.1. Introduction -- 7.2. Context -- 7.2.1. Configuration -- 7.2.2. Recommendation -- 7.2.3. Obstacles and challenges of interactive PL configuration -- 7.3. Overview of the proposed approach -- 7.4. Preliminary evaluation -- 7.5. Discussion and related work -- 7.5.1. Recommendation techniques -- 7.6. Conclusion and future work -- 7.7. Bibliography.
8: Semio-Cognitive Spaces: the Frontier of Recommender Systems -- 8.1. Introduction -- 8.2. Latest developments: finalized activities, recommender systems and the relevance of information -- 8.2.1. Cognitive dynamics of finalized activities -- 8.2.2. The foundations of recommender systems -- 8.2.2.1. Content-based recommender systems -- 8.2.2.2. Collaboration-based recommender systems -- 8.2.2.3. Knowledge-based recommender systems -- 8.2.2.4. The evaluation of RSs -- 8.2.3. What information relevance? -- 8.3. Observable interests for decision theory: a combination of content-based, collaboration-based and knowledge-based recommendations -- 8.3.1. Methodology: meta-analysis and modeling of the process -- 8.3.2. Analysis and modeling of a macro-process for responding to a call for R&amp -- D projects -- 8.3.2.1. On a technical architecture level -- 8.3.2.2. On an informational need and object flow level -- 8.3.2.3. Suggestions in terms of RSs -- 8.3.3. Analysis and model of a socio-organizational tool for the management of customer complaints -- 8.3.3.1. At the process level -- 8.3.3.2. On a technical architecture level -- 8.3.3.3. On an informational need and object flow level -- 8.3.3.4. Suggestions in terms of RSs -- 8.4. Discussion and conclusions -- 8.4.1. Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance -- 8.5. Conclusions: recommender systems linked to finalized activities -- 8.5.1. The localization of activities and geographical information systems: a new kind of data -- 8.5.2. Transparency of the use of personal data, data protection and ownership -- 8.6. Acknowledgments -- 8.7. Bibliography -- 9: The French-Speaking Literary Prescription Market in Networks -- 9.1. Introduction -- 9.2. The economy of prescription -- 9.2.1. The notion of prescription.
9.2.2. From the advisors market to the prescription market -- 9.3. Methodology -- 9.4. The competitive structure of the market of online social networks of readers -- 9.4.1. Pure player networks and the audience strategy -- 9.4.2. Amateur networks and the survival strategy -- 9.4.3. Backed networks and the hybridization strategy -- 9.5. The organization of prescription -- 9.5.1. Social prescription -- 9.5.2. Editorial prescription -- 9.5.3. Algorithmic prescription -- 9.6. Conclusion: what legitimacy for literary prescription? -- 9.7. Appendix: list of interviews undertaken -- 9.8. Bibliography -- 10: Presentation of Offered Services: Babelio, a Recommendation Engine Dedicated to Books -- 10.1. Introduction -- 10.2. The problem of qualitative pertinence -- 10.3. The problem of quantitative pertinence -- 10.4. Balancing recall and precision -- 10.5. The issue of sparse data -- 10.6. Performance and scalability -- 10.7. A few issues specific to books -- 11: Presentation of the Offer of Services: Nomao, Recommender Systems and Information Search -- 11.1. Introduction: the actors of Internet recommendation -- 11.2. Approaches to recommendation -- 11.3. Nomao: a local outlets search and recommendation engine -- 11.3.1. Popularity score -- 11.3.2. Affinity score -- 11.3.2.1. Collaborative filtering -- 11.3.2.2. Descriptive profiling -- 11.3.3. Social recommendation -- 11.4. Prospects: the move toward interactive recommender systems -- 11.5. Appendix -- List of Authors -- Index.
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Cover -- Title Page -- Copyright -- Contents -- Preface: Recommender Engines (and Systems) -- Acknowledgments -- Bibliography -- 1: General Introduction to Recommender Systems -- 1.1. Putting it into perspective -- 1.2. An interdisciplinary subject -- 1.3. The fundamentals of algorithms -- 1.3.1. Collaborative filtering -- 1.3.1.1. Advantages and drawbacks of collaborative filtering -- 1.3.2. Content filtering -- 1.3.2.1. Advantages and drawbacks of content filtering -- 1.3.3. Hybrid methods -- 1.3.4. Conclusion on historical recommendation models -- 1.4. Content offers and recommender systems -- 1.4.1. Culture and recommender systems -- 1.4.1.1. Recommendation and cinema -- 1.4.1.2. Recommendation and literature -- 1.4.1.3. Recommendation and general culture -- 1.4.2. Recommender systems and the e-commerce of content -- 1.4.3. The behavior of users -- 1.5. Current issues -- 1.6. Bibliography -- 2: Understanding Users' Expectations for Recommender Systems: the Case of Social Media -- 2.1. Introduction: the omnipresence of recommender systems -- 2.2. The social approach to prescription -- 2.2.1. The theory of the prescription and online interactions -- 2.2.2. Conditions for recognition of the prescription -- 2.2.3. The specificities of social media -- 2.3. Users who do not focus on the prescriptions of platforms -- 2.3.1. Facebook: the link, the type of activity and the context -- 2.3.2. Twitter: prescription between peers and explanation of prescription -- 2.3.3. Conditions for the recognition of a prescription: announcement and enunciation -- 2.4. A guide for considering recommender systems adapted to different forms of social media -- 2.5. Conclusion -- 2.6. Bibliography -- 3: Recommender Systems and Social Networks: What Are the Implications for Digital Marketing? -- 3.1. Social recommendations: an ancient practice revived by the digital age.

3.1.1. Recommendations: a difficult management for brands -- 3.1.2. Internet recommendations: social presence and personalized recommendations -- 3.2. Social recommendations: how are they used for e-commerce? -- 3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites -- 3.2.2. Recommender systems used by social networks: from e-commerce to social commerce -- 3.2.2.1. Facebook, innovator in its vision for social recommendation: Like, Edge Rank, Place, Social and Open Graph -- 3.2.2.2. Social recommendation, the cornerstone of an emerging social commerce -- 3.3. Conclusion -- 3.4. Bibliography -- 4: Recommender Systems and Diversity: Taking Advantage of the Long Tail and the Diversity of Recommendation Lists -- 4.1. The stakes associated with diversity within recommender systems -- 4.1.1. Individual diversity or the individual perception of diversity -- 4.1.2. The stakes and impacts of aggregate diversity -- 4.1.2.1. Markets with limited resources -- 4.1.2.2. Cultural diversity -- 4.1.2.3. The long-tail economy: toward a more diverse consumption -- 4.2. Recommendation algorithms and diversity: trends, evaluation and optimization -- 4.2.1. The tendency for recommendation algorithms to focus on the head -- 4.2.2. The evaluation of diversity in recommender systems -- 4.2.3. Recommendation algorithms which favor individual diversity -- 4.2.4. Recommendation algorithms which favor aggregate diversity -- 4.2.5. The shift toward user-centered diversity approaches -- 4.2.5.1. The perception of diversity by users -- 4.2.5.2. Analyzing users in order to improve aggregate diversity -- 4.3. Conclusion and new directions -- 4.4. Bibliography -- 5: iSoNTRE: Intelligent Transformer of Social Networks into a Recommendation Engine Environment -- 5.1. Summary -- 5.2. Introduction -- 5.3. Latest developments, definition and history.

5.3.1. Collaborative filtering techniques -- 5.3.2. General use social networks: what do they contain? -- 5.3.3. Social recommendation -- 5.3.4. The recommendation of concepts -- 5.4. iSoNTRE -- 5.4.1. iSoNTRE: transformer of social networks -- 5.4.1.1. Extracting concepts from profiles -- 5.4.1.1.1. Disambiguation -- 5.4.1.1.2. World sources of knowledge -- 5.4.1.2. Adding friends to the N-Facet model -- 5.4.2. iSoNTRE: the core of recommendation -- 5.4.2.1. Constructing the general matrix and the matrices specific to domains -- 5.4.2.2. Autonomous recommendation -- 5.4.2.3. Combined recommender systems -- 5.5. Experiments -- 5.5.1. The preparation of data -- 5.5.2. Testing methodology -- 5.5.3. The creation of avatars -- 5.5.4. Results -- 5.5.5. Discussion -- 5.6. Conclusion -- 5.7. Bibliography -- 6: A Two-Level Recommendation Approach for Document Search -- 6.1. Introduction -- 6.2. Tag recommendation: a brief state of the art -- 6.3. The hypertagging system -- 6.3.1. Metadata -- 6.3.2. Architecture -- 6.4. Recommendation approach -- 6.4.1. Presentation -- 6.4.1.1. Correlation between facets -- 6.4.1.2. Correlation between tags -- 6.4.2. Recommendation algorithm -- 6.5. Evaluation -- 6.5.1. Generation of facets -- 6.5.2. Generation of association rules -- 6.5.3. Evaluation of recommendation rules -- 6.6. Conclusion -- 6.7. Bibliography -- 7: Combining Configuration and Recommendation to Enable an Interactive Guidance of Product Line Configuration -- 7.1. Introduction -- 7.2. Context -- 7.2.1. Configuration -- 7.2.2. Recommendation -- 7.2.3. Obstacles and challenges of interactive PL configuration -- 7.3. Overview of the proposed approach -- 7.4. Preliminary evaluation -- 7.5. Discussion and related work -- 7.5.1. Recommendation techniques -- 7.6. Conclusion and future work -- 7.7. Bibliography.

8: Semio-Cognitive Spaces: the Frontier of Recommender Systems -- 8.1. Introduction -- 8.2. Latest developments: finalized activities, recommender systems and the relevance of information -- 8.2.1. Cognitive dynamics of finalized activities -- 8.2.2. The foundations of recommender systems -- 8.2.2.1. Content-based recommender systems -- 8.2.2.2. Collaboration-based recommender systems -- 8.2.2.3. Knowledge-based recommender systems -- 8.2.2.4. The evaluation of RSs -- 8.2.3. What information relevance? -- 8.3. Observable interests for decision theory: a combination of content-based, collaboration-based and knowledge-based recommendations -- 8.3.1. Methodology: meta-analysis and modeling of the process -- 8.3.2. Analysis and modeling of a macro-process for responding to a call for R&amp -- D projects -- 8.3.2.1. On a technical architecture level -- 8.3.2.2. On an informational need and object flow level -- 8.3.2.3. Suggestions in terms of RSs -- 8.3.3. Analysis and model of a socio-organizational tool for the management of customer complaints -- 8.3.3.1. At the process level -- 8.3.3.2. On a technical architecture level -- 8.3.3.3. On an informational need and object flow level -- 8.3.3.4. Suggestions in terms of RSs -- 8.4. Discussion and conclusions -- 8.4.1. Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance -- 8.5. Conclusions: recommender systems linked to finalized activities -- 8.5.1. The localization of activities and geographical information systems: a new kind of data -- 8.5.2. Transparency of the use of personal data, data protection and ownership -- 8.6. Acknowledgments -- 8.7. Bibliography -- 9: The French-Speaking Literary Prescription Market in Networks -- 9.1. Introduction -- 9.2. The economy of prescription -- 9.2.1. The notion of prescription.

9.2.2. From the advisors market to the prescription market -- 9.3. Methodology -- 9.4. The competitive structure of the market of online social networks of readers -- 9.4.1. Pure player networks and the audience strategy -- 9.4.2. Amateur networks and the survival strategy -- 9.4.3. Backed networks and the hybridization strategy -- 9.5. The organization of prescription -- 9.5.1. Social prescription -- 9.5.2. Editorial prescription -- 9.5.3. Algorithmic prescription -- 9.6. Conclusion: what legitimacy for literary prescription? -- 9.7. Appendix: list of interviews undertaken -- 9.8. Bibliography -- 10: Presentation of Offered Services: Babelio, a Recommendation Engine Dedicated to Books -- 10.1. Introduction -- 10.2. The problem of qualitative pertinence -- 10.3. The problem of quantitative pertinence -- 10.4. Balancing recall and precision -- 10.5. The issue of sparse data -- 10.6. Performance and scalability -- 10.7. A few issues specific to books -- 11: Presentation of the Offer of Services: Nomao, Recommender Systems and Information Search -- 11.1. Introduction: the actors of Internet recommendation -- 11.2. Approaches to recommendation -- 11.3. Nomao: a local outlets search and recommendation engine -- 11.3.1. Popularity score -- 11.3.2. Affinity score -- 11.3.2.1. Collaborative filtering -- 11.3.2.2. Descriptive profiling -- 11.3.3. Social recommendation -- 11.4. Prospects: the move toward interactive recommender systems -- 11.5. Appendix -- List of Authors -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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