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
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.
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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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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