User-oriented Web Search Based on PLSA
Fang Yu, Chen Dong-ling, Daling Wang, Yu Ge, Yubin Bao · Journal of Southeast University · 2007
In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines' inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis (PLSA) is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user's topics of interest vector is created by analyzing the user's click through data based on PLSA, then the user's queries are mapped into categories based on the user's preferences, and finally the result list is re-ranked according to the user's interests based on the new proposed method named user-oriented PageRank (UOPR). Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user's personalized information needs.