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Towards more targeted recommendations in folksonomies

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Abstract

Recommender systems are now popular both commercially as well as within the research community, where many approaches have been suggested for providing recommendations. Folksonomies’ users are sharing items (e.g., movies, books, and bookmarks) by annotating them with freely chosen tags. Within the Web 2.0 age, users become the core of the system since they are both the contributors and the creators of the information. In this respect, it is of paramount importance to match their needs for providing a more targeted recommendation. In this paper, we consider a new dimension in a folksonomy classically composed of three dimensions <users,tags,resources> and propose an approach to group users with close interests through quadratic concepts. Then, we use such structures in order to propose our personalized recommendation system of users, tags, and resources. We carried out extensive experiments on two real-life datasets, i.e., MovieLens and BookCrossing which highlight good results in terms of precision and recall as well as a promising social evaluation. Moreover, we study some of the key assessment metrics namely coverage, diversity, adaptivity, serendipity, and scalability. Finally, we conduct a user study as a valuable complement to our evaluation in order to get further insights.

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Notes

  1. Downloadable at this link http://www.isima.fr/~mephu/FILES/FolkRec/.

  2. http://movielens.umn.edu/.

  3. http://www.grouplens.org/node/73.

  4. http://www.bookcrossing.com/.

  5. http://www.grouplens.org/node/74.

  6. From the 13625 cities represented in BookCrossing, we evaluate the coverage of FolkRec above the most represented ones, i.e., cities present in more than 500 quadruples in the v-folksonomy.

  7. We omit the tag suggestion task since that BookCrossing rather considers ratings than tags.

  8. Unfortunately, the codes of our competitors are not available. Moreover, The runtime of the competitors were not specified in the original papers.

  9. Pertinent resources (resp. tags or users) are those (resp. tags or users) recommended by FolkRec.

References

  • Agarwal D, Chen B-C (2010) fLDA: matrix factorization through latent dirichlet allocation. In: Proceedings of the third ACM international conference on web search and data mining, WSDM ’10, ACM, New York, pp 91–100

  • Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston

    Google Scholar 

  • Basile P, Gendarmi D, Lanubile F, Semeraro G (2007) Recommending smart tags in a social bookmarking system. Bridg Gap Between Sem Web Web 2:22–29

    Google Scholar 

  • Bellogín A, Cantador I, Castells P (2013) A comparative study of heterogeneous item recommendations in social systems. Inf Sci 221:142–169

    Article  Google Scholar 

  • Breuss M, Tsagkias M (2014) Learning from user interactions for recommending content in social media. In: 36th European conference on information retrieval (ECIR’14)

  • Cerf L, Besson J, Nguyen K, Boulicaut J (2013) Closed and noise-tolerant patterns in n-ary relations. Data Min Knowl Discov 26(3):574–619

    Article  MATH  MathSciNet  Google Scholar 

  • Cerf L, Besson J, Robardet C, Boulicaut J-F (2009) Closed patterns meet n-ary relations, ACM TKDD 3 3:1–3:36

  • Das M, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C (2012) Who tags what? An analysis framework. Proc PVLDB 5(11):1567–1578

    Google Scholar 

  • De Meo P, Quattrone G, Ursino D (2010) A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy. User Model User-Adapt Interact 20(1):41–86

    Article  Google Scholar 

  • Diederich J, Iofciu T (2006) Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st international workshop on TEL-CoPs, Crete, Greece, pp 288–297

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 2004:5–53

    Article  Google Scholar 

  • Hu J, Wang B, Tao Z (2011) Personalized tag recommendation using social contacts. In: Proceedings. of workshop SRS’11, in conjunction with CSCW

  • Jelassi MN, Ben Yahia S, Mephu Nguifo E (2012) A scalable mining of frequent quadratic concepts in d-folksonomies, ArXiv e-printsarXiv:1212.0087

  • Jelassi MN, Ben Yahia S, Mephu Nguifo E (2013) A personalized recommender system based on users’ information in folksonomies. In: Proceedings of the 22nd international conference on world wide web companion, WWW ’13 Companion, pp 1215–1224

  • Jäschke R, Hotho A, Schmitz C, Ganter B, Stumme G (2008) Discovering shared conceptualizations in folksonomies. Web Sem 6:38–53

    Article  Google Scholar 

  • Jäschke R, Marinho L, Hotho AA, Lars S-T, Stum G (2007) Tag recommendations in folksonomies. In: Proceedings of the 11th ECML PKDD, Warsaw, Poland, pp 506–514

  • Kim HK, Oh HY, Gu JC, Kim JK (2011) Commenders: a recommendation procedure for online book communities. Electron Commer Rec Appl 10(5):501–509

    Article  Google Scholar 

  • Landia N, Anand S (2009) Personalised tag recommendation. Recommender Systems & the Social Web, New York

    Google Scholar 

  • Liang H (2010) User profiling based on folksonomy information in web 2.0 for personalized recommender systems, Ph.D. thesis, Queensland University of Technology

  • Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41(4):2065–2073

    Article  Google Scholar 

  • Lipczak M (2008) Tag recommendation for folksonomies oriented towards individual users. In: Proceedings of the ECML/PKDD discovery challenge, Antwerp, Belgium, pp 84–95

  • Mika P (2007) Ontologies are us: a unified model of social networks and semantics. J Web Sem 5(1):5–15

    Article  MathSciNet  Google Scholar 

  • Noll M, Michael G, Meinel C (2007) Web search personalization via social bookmarking and tagging. In: Proceedings of the 6th ISWC/ASWC, Busan, Korea, pp 367–380

  • Qumsiyeh R, Ng Y-K (2012) Predicting the ratings of multimedia items for making personalized recommendations. In: SIGIR’12, ACM, New York, pp 475–484

  • Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, New York

    MATH  Google Scholar 

  • Rikitianskii A, Harvey M, Crestani F (2014) A personalised recommendation system for context-aware suggestions. In: Advances in Information Retrieval. Lecture Notes in Computer Science, vol 8416. Springer, New York, pp 63–74

  • Said A, Bellogín A (2014) Comparative recommender system evaluation: benchmarking recommendation frameworks. In: Proceedings of the 8th ACM conference on recommender systems, RecSys, ACM, ACM, Foster City

  • Said A, Kille B, De Luca EW, Albayrak S (2011) Personalizing tags: a folksonomy-like approach for recommending movies. In: 2nd international HetRec, pp 53–56

  • Strohmaier M, KöRner C, Kern R (2012) Understanding why users tag: a survey of tagging motivation literature and results from an empirical study. Web Sem 17:1–11

    Article  Google Scholar 

  • Trabelsi C, Jelassi N, Ben Yahia S (2012) Scalable mining of frequent tri-concept. In: Proceedings of the 15th PAKDD, Kuala Lampur, Malaysia, pp 231–242

  • Vallet D, Cantador I, Jose JM (2010) Personalizing web search with folksonomy-based user and document profiles. In: Proceedings of the 32nd ECIR, Berlin, Heidelberg, pp 420–431

  • Valtchev P, Hacene MR, Missaoui R (2003) A generic scheme for the design of efficient on-line algorithms for lattices. In: ICCS, pp 282–295

  • Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  • Yang S-H, Long B, Smola AJ, Zha H, Zheng Z (2011) Collaborative competitive filtering: Learning recommender using context of user choice. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’11, ACM, New York, pp 295–304

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Acknowledgments

Thanks to the PHC Utique project EXQUI 11G1417 for financial support to the first author, as well as project (French-Brazilian GDRI “Web of Sciences”) for fruitful discussion on this topic. We also thank the anonymous referees for their valuable remarks and suggestions.

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Correspondence to Mohamed Nader Jelassi.

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Jelassi, M.N., Ben Yahia, S. & Mephu Nguifo, E. Towards more targeted recommendations in folksonomies. Soc. Netw. Anal. Min. 5, 68 (2015). https://doi.org/10.1007/s13278-015-0307-8

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  • DOI: https://doi.org/10.1007/s13278-015-0307-8

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