TITLE:
Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering
AUTHORS:
Shaowu Zhang, Yanyan Ge
KEYWORDS:
Tag Recommendation, Collaborative Filtering, Transfer Tensor
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.9,
September
7,
2015
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.