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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Downloadable at this link http://www.isima.fr/~mephu/FILES/FolkRec/.
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.
We omit the tag suggestion task since that BookCrossing rather considers ratings than tags.
Unfortunately, the codes of our competitors are not available. Moreover, The runtime of the competitors were not specified in the original papers.
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
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
Bellogín A, Cantador I, Castells P (2013) A comparative study of heterogeneous item recommendations in social systems. Inf Sci 221:142–169
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
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
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
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
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
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
Landia N, Anand S (2009) Personalised tag recommendation. Recommender Systems & the Social Web, New York
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
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
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
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
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
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
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-015-0307-8