article

Mediation of user models for enhanced personalization in recommender systems

Authors:

Shlomo Berkovsky,

Francesco RicciAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 18, Issue 3

Pages 245 - 286

https://doi.org/10.1007/s11257-007-9042-9

Published: 01 August 2008 Publication History

Abstract

Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.

References

[1]

Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. 23(1), 103-145 (2005).

Digital Library

[2]

Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Eng. 17(6), 734-749 (2005a).

Digital Library

[3]

Adomavicius, G., Tuzhilin, A.: Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83-90 (2005b).

Digital Library

[4]

Aguzzoli, S., Avesani, P., Massa, P.: Collaborative case-based recommender system. In: European Conference on Case-Based Reasoning, pp. 460-474. Aberdeen, UK (2002).

Digital Library

[5]

Aroyo, L., Schut, H., Nack, F., Schiphorst, T., Kauw-A-Tjoe, M.: Personalized ambient media experience: move.me case study. In: International Conference on Intelligent User Interfaces, pp. 298-301. Honolulu, HI (2007).

[6]

Berkovsky, S.: Decentralized mediation of user models for a better personalization. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 404-408. Dublin, Ireland (2006).

[7]

Berkovsky, S., Kuflik, T., Ricci, F.: Cross-technique mediation of user models. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 21-30. Dublin, Ireland (2006a).

[8]

Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Recommender Systems Conference Minneapolis, MN, (2007c).

[9]

Berkovsky, S., Aroyo, L., Heckmann, D., Houben, G.J., Kröner, A., Kuflik, T., Ricci, F.: Providing context-aware personalization through cross-context reasoning of user modeling data. In: Workshop on Decentralized and Ubiquitous User Modeling, pp. 2-7. Corfu, Greece (2007a).

[10]

Berkovsky, S., Gorfinkel, A., Kuflik, T., Manevitz, L.: Case-based to content-based user model mediation. In: Workshop on Ubiquitous User Modeling, pp. 1-4. Riva del Garda, Italy (2006b).

[11]

Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: Recommender Systems Conference, Minneapolis, MN (2007b).

[12]

Brown, P.J., Bovey, J.D., Chen, X.: Context-aware applications: from the laboratory to the marketplace. IEEE Pers. Commun. 4(5), 58-64 (1997).

[13]

Buriano, L., Marchetti, M., Carmagnola, F., Cena, F., Gena, C., Torre, I.: The role of ontologies in context-aware recommender systems. In: International Conference on Mobile Data Management, p. 80. Nara, Japan (2006).

[14]

Burke, R.: Knowledge-based recommender systems. In: Kent A. (ed.) Encyclopedia of Library and Information Systems, pp. 180-200. Dekker Encyclopedias (2000).

[15]

Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331-370 (2002).

Digital Library

[16]

Chen, A.: Context-aware collaborative filtering system: predicting the user's preference in the ubiquitous computing environment. In: International Workshop on Location- and Context-Awareness, pp. 244-253. Oberpfaffenhofen, Germany (2005).

[17]

Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Workshop on Recommender Systems, Berkeley, CA, http://www.cs.umbc.edu/~ian/sigir99-rec/ (1999).

[18]

Cranor, L.F., Reagle, J., Ackerman, M.S.: Beyond concern: understanding net users' attitudes about online privacy. Technical Report, AT&T Labs-Research (1999).

[19]

Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing, pp. 304-307. Karlsruhe, Germany (1999).

[20]

Finin, T.W., Drager, D.: A general user modeling system. In: Canadian Conference on Artificial Intelligence, pp. 24-29. Montreal, Canada (1986).

[21]

Finin, T.W.: GUMS: a general user modeling shell. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, pp. 411-430. Springer Publishers (1989).

[22]

Francisco-Revilla, L., Shipman, M.: Managing conflict in multi-model adaptive hypertext. In: Conference on Hypertext and Hypermedia, pp. 237-238. Santa Cruz, CA (2004).

[23]

Goker, A., Myrhaug, H.I.: User context and personalization. In: Workshop on Case Based Reasoning and Personalization, pp. 4-7. Aberdeen, UK (2002).

[24]

Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inform. Retr. 4(2), 133-151 (2001).

Digital Library

[25]

Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61-70 (1992).

Digital Library

[26]

Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B. Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: National Conference on Artificial Intelligence, pp. 439-446. Orlando, FL (1999).

Digital Library

[27]

Hanani, U., Shapira, B., Shoval, P.: Information filtering: overview of issues, research and systems. User Model. User Adapt. Interact. 11(3), 203-259 (2001).

[28]

Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., Wilamowitz-Moellendorff, M.V.: GUMO--the general user model ontology. In: International Conference on User Modeling, pp. 428-432. Edinburgh, UK (2005).

Digital Library

[29]

Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: International Conference on Research and Development in Information Retrieval, pp. 230-237. Berkeley, CA (1999).

[30]

Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Syst. 22(1), 5-53 (2004) Internet Movie Database, http://www.imdb. com

Digital Library

[31]

Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: a tour guide for the World Wide Web. In: International Joint Conference on Artificial Intelligence, pp. 770-777. Nagoya, Japan (1997).

[32]

Keidl, M., Kemper, A.: Towards context-aware adaptable web services. In: International World-Wide Web Conference, pp. 55-65. New York, NY (2004).

[33]

Kay, J.: Ontologies for reusable and scrutable student model. In: Workshop on Workshop on Ontologies for Intelligent Educational Systems, pp. 72-77. Le Mans, France (1999).

[34]

Kay, J., Kummerfeld, B., Lauder, P.: Managing private user models and shared personas. In: Workshop on User Modeling in Ubiquitous Computing, pp. 1-11. Pittsburgh, PA (2003).

[35]

Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20(3), 226-239 (1998).

Digital Library

[36]

Kobsa, A.: Generic User Modeling Systems. User Model. User-Adapt. Interact. 11(1-2), 49-63 (2001).

[37]

Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowledge Eng. Rev. 16(2), 111-155 (2001).

Digital Library

[38]

Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. J. 97(1-2), 273-324 (1997).

Digital Library

[39]

Krulwich, B.: Lifestyle finder: intelligent user profiling using large-scale demographic data. Artif. Intell. Mag. 18(2), 37-45 (1997).

[40]

Kuflik, T., Sheidin, J., Jbara, S., Goren-Bar, D., Soffer, P., Stock, O., Zancanaro M.: Supporting small groups in the museum by context-aware communication services. In: International Conference on Intelligent User Interfaces, pp. 305-308. Honolulu, HI (2007).

[41]

Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative filtering and inference rules for context-aware learning object recommendation. J. Interact. Technol. Smart Educ. 2(3), 179-188 (2005).

[42]

Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76-80 (2003).

Digital Library

[43]

Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 31-40 (1994).

Digital Library

[44]

Manouselis, N., Sampson, D.: A multi-criteria model to support automatic recommendation of e-learning quality approaches. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 518-526. Chesapeake, VA (2004).

[45]

McJones, P.: EachMovie Collaborative Filtering Data Set. HP Research (1997).

[46]

McNee, S.M., Lam, S., Konstan, J., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: International Conference on User Modeling, pp. 178-183. Pittsburgh, PA (2003).

Digital Library

[47]

Mehta, B., Niederée, C., Stewart, A., Degemmis, M., Lops, P., Semeraro, G.: Ontologically enriched user profiling for cross system personalization. In: International Conference on User Modeling, pp. 119-123. Edinburgh, UK (2005).

[48]

Montaner, M., Lopez, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. AI Rev. 19(4), 285-330 (2003).

Digital Library

[49]

Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match retrieval. In: International Conference on Research and Development in Information Retrieval, pp. 272-281. Dublin, Ireland (1994).

[50]

Mulvenna, M.D., Anand, S.S., Buchner, A.G.: Personalization on the net using web mining. Commun. ACM 43(8), 123-125 (2000).

Digital Library

[51]

Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Investigation for designing of context-aware recommendation system using SVM. In: International MultiConference of Engineers and Computer Scientists, pp. 970-975. Hong Kong, Hong Kong (2007).

[52]

Pascoe, J.: Adding generic contextual capabilities to wearable computers. In: International Symposium on Wearable Computers, pp. 92-99. Pittsburgh, PA (1998).

[53]

Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393-408 (1999).

Digital Library

[54]

Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313-331 (1997).

Digital Library

[55]

Potonniee, O.: A decentralized privacy-enabling TV personalization framework. In: European Conference on Interactive Television, pp. 57-66. Brighton, UK (2002).

[56]

Rabanser, U., Ricci, F.: Recommender systems: do they have a viable business model in e-tourism? In: Conference of the International Federation for IT & Travel and Tourism, pp. 160-171. Lausanne, Switzerland (2005).

[57]

Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based user modeling for knowledge management systems. In: International Conference on User Modeling, pp. 213-217. Pittsburgh, PA (2003).

Digital Library

[58]

Resource Description Framework, http://www.w3c.org/RDF/

[59]

Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of Netnews. In: International Conference on Computer-Supported Collaborative Work, pp. 175-186. Chapel Hill, NC (1994).

[60]

Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56-58 (1997).

Digital Library

[61]

Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: a case-based travel advisory system. In: European Conference on Case-Based Reasoning, pp. 613-627. Aberdeen, UK (2002).

[62]

Ricci, F.: Recommendations in context. In: International Conference on Electronic Commerce and Web Technologies, Krakow, Poland, keynote talk (2006).

[63]

Ricci, F., Cavada, D., Mirzadeh, N., Venturini, A.: Case-based travel recommendations. In: Fesemaier, D.R., Werthner, H., Wöber, K.W. (eds.) Destination Recommendation Systems: Behavioural Foundations and Applications, pp. 67-93. CABI publishers (2006).

[64]

Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles for user behaviors. Comput. Networks ISDN Syst. 29(8-13), 1447-1455 (1997).

Digital Library

[65]

Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill publishers (1983).

[66]

Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering algorithms. In: International World-Wide Web Conference, pp. 285-295. Hong Kong, Hong Kong (2001).

Digital Library

[67]

Schafer, J.B., Konstan, J., Riedl, J.: Electronic commerce recommender applications. J. Data Mining Knowledge Discov. 5(1-2), 115-152 (2000).

[68]

Setten, M.V., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 235-244. Eindhoven, the Netherlands (2004).

[69]

Shardanand, U., Maes, P.: Social information filtering: algorithms for automating 'word of mouth'. In: International Conference on Human Factors in Computing Systems, pp. 210-217. Pittsburgh, PA, (1995).

[70]

Vozalis, M., Margaritis, K.G.: Enhancing collaborative filtering with demographic data: the case of item-based filtering. In: International Conference on Intelligent Systems Design and Applications, pp. 361-366. Budapest, Hungary (2004).

[71]

Wahlster, W., Kröner, A., Heckmann, D.: SharedLife: towards selective sharing of augmented personal memories. In: Sotck, O., Schaerf, M. (eds.) Reasoning, Action and Interaction in AI Theories and Systems, pp. 327-342. Springer Publishers (2006).

[72]

Wang, Y., Kobsa, A.: Impacts of privacy laws and regulations on personalized systems. In: Workshop on Privacy-Enhanced Personalization, pp. 44-46. Montreal, Canada (2006).

[73]

Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: International World-Wide Web Conference, pp. 22-32. Chiba, Japan (2005).

Cited By

Wang YXie QTang MLi LYuan JLiu Y(2024)A Dual Perspective Framework of Knowledge-correlation for Cross-domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/365252018:6(1-28)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652520
Liu JSun LNie WSu YZhang YLiu A(2024)Inter- and Intra-Domain Potential User Preferences for Cross-Domain RecommendationIEEE Transactions on Multimedia10.1109/TMM.2024.337457726(8014-8025)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3374577
Guo LLiu HZhu LGuan WCheng Z(2023)DA-DAN: A Dual Adversarial Domain Adaption Network for Unsupervised Non-overlapping Cross-domain RecommendationACM Transactions on Information Systems10.1145/361782542:2(1-27)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3617825
Show More Cited By

Index Terms

Mediation of user models for enhanced personalization in recommender systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
  2. Interaction design
    1. Interaction design theory, concepts and paradigms
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

Recommendations

Cross-representation mediation of user models

Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
User Personality and User Satisfaction with Recommender Systems

In this study, we show that individual users' preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 18, Issue 3

August 2008

70 pages

ISSN:0924-1868

Issue’s Table of Contents

Copyright © Copyright © 2008 Springer Science+Business Media B.V.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2008

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang YXie QTang MLi LYuan JLiu Y(2024)A Dual Perspective Framework of Knowledge-correlation for Cross-domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/365252018:6(1-28)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652520
Liu JSun LNie WSu YZhang YLiu A(2024)Inter- and Intra-Domain Potential User Preferences for Cross-Domain RecommendationIEEE Transactions on Multimedia10.1109/TMM.2024.337457726(8014-8025)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3374577
Guo LLiu HZhu LGuan WCheng Z(2023)DA-DAN: A Dual Adversarial Domain Adaption Network for Unsupervised Non-overlapping Cross-domain RecommendationACM Transactions on Information Systems10.1145/361782542:2(1-27)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3617825
Vente T(2023)Advancing Automation of Design Decisions in Recommender System PipelinesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608886(1355-1360)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608886
Wegmeth L(2023)Improving Recommender Systems Through the Automation of Design DecisionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608877(1332-1338)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608877
Tran TFelfernig ALe VChau TMai T(2023)User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal CharacteristicsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592950(54-65)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592950
Sun WMa MRen PLin YChen ZRen ZMa Jde Rijke M(2023)Parallel Split-Join Networks for Shared Account Cross-Domain Sequential RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313092735:4(4106-4123)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3130927
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3487331
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1007/s11257-022-09335-w
Zhang XGong BCao YDing YSu J(2022)Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approachElectronic Commerce Research10.1007/s10660-020-09408-122:3(811-842)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10660-020-09408-1
Show More Cited By

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents