A Social and Popularity-based Tag Recommender

Abstract : Tag recommendation aims to recommend to a user the most suited tags for a given item. It is an important functionality of resource sharing systems. In this paper we propose a recommendation algorithm, called FasTag, that links the relevance of the tags to both their popularity and the opinions of the user's neighbors. FasTag assumes that the users are organized in a weighted graph representing, for instance, the similarity or the trust between them. The two salient aspects of FasTag are the scoring function it uses to evaluate the relevance of the tags, and its low computation cost. Thus, FasTag can make online recommendations, even for large datasets. Moreover, we improve the accuracy of its recommendations by adjusting automatically the size of the recommended list of tags. The experiments we did on several datasets show a significant improvement of the accuracy of the recommendations.
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Communication dans un congrès
SocialCom 2014 - The Seventh IEEE International Conference on Social Computing and Networking , Dec 2014, Sidney, Australia. IEEE, Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on, pp.318-325, 2014, 〈10.1109/BDCloud.2014.44〉
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https://hal-imt.archives-ouvertes.fr/hal-01113540
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Soumis le : jeudi 5 février 2015 - 17:55:45
Dernière modification le : jeudi 12 avril 2018 - 16:34:05

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Modou Gueye, Talel Abdessalem, Hubert Naacke. A Social and Popularity-based Tag Recommender. SocialCom 2014 - The Seventh IEEE International Conference on Social Computing and Networking , Dec 2014, Sidney, Australia. IEEE, Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on, pp.318-325, 2014, 〈10.1109/BDCloud.2014.44〉. 〈hal-01113540〉

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