Dynamic Adaptation Strategies for Long-Term and Short-Term User Profile to Personalize Search
edited by: Guozhu Dong, Xuemin Lin, Wei Wang, Yun Yang, Jeffrey Yu
Recent studies on personalized search have shown that user preferences could be learned implicitly. As far as we know, these studies, however, neglect that user preferences are likely to change over time. This paper introduces an adaptive scheme to learn the changes of user preferences from click-history data, and a novel rank mechanism to bias the search results of each user. We propose independent models for long-term and short-term user preferences to compose our user profile. The proposed user profile contains a taxonomic hierarchy for the long-term model and a recently visited page-history buffer for the short-term model. Dynamic adaptation strategies are devised to capture the accumulation and degradation changes of user preferences, and adjust the content and the structure of the user profile to these changes. Experimental results demonstrate that our scheme is efficient to model the up-to-date user profile, and that the rank mechanism based on this scheme can support web search systems to return the adequate results in terms of the user satisfaction, yielding about 29.14% average improvement over the compared rank mechanisms in experiments.