Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering

DOI: 10.4236/jcc.2015.39002   PDF   HTML   XML   3,386 Downloads   4,095 Views   Citations

Abstract

In social tagging systems, users are allowed to label resources with tags, and thus the system builds a personalized tag vocabulary for every user based on their distinct preferences. In order to make the best of the personalized characteristic of users’ tagging behavior, firstly the transfer matrix is used in this paper, and the tag distributions of query resources are mapped to users’ query before the recommendation. Meanwhile, we find that only considering the user’s preference model, the method cannot recommend new tags for users. So we utilize the thought of collaborative filtering, and produce the recommend tags based on the query user and his/her nearest neighbors' preference models. The experiments conducted on the Delicious corpus show that our method combining transfer matrix with collaborative filtering produces better recommendation results.

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Zhang, S. and Ge, Y. (2015) Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering. Journal of Computer and Communications, 3, 9-17. doi: 10.4236/jcc.2015.39002.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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