Personalized search on the world wide web
The adaptive web
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
An information recommendation agent on microblogging service
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Personalized diversification of search results
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Retweet or not?: personalized tweet re-ranking
Proceedings of the sixth ACM international conference on Web search and data mining
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According to the remarkable progress of Web technologies, people search various types of items such as movies, books, and CDs on the Internet. Items that are returned as search results would be ranked based on objective criteria such as rating scores. Such rankings are not personalized to a user based on his/her interests and preferences. In this paper, we introduce a method for ranking items that are specified by users. Our method does not request users to specify their interests and preference manually. Our method focuses on the users who have posted tweets about the items and extracts features of the users. Items are ranked according to the preferences of the user based on the similarities of twitter users. The effectiveness of the proposed method is evaluated with experimental results on subjects.