Estimating Personalization using Topical User Profile.

Exploring the effect of personalization on different queries can improve the ranking result. There is a need for a mechanism to estimate the potential for personalization for queries. Previous methods to estimate the potential for personalization such as click entropy and topic entropy are based on the prior clicked document for query or query history. They have limitations like unavailability of the prior clicked data for new/unseen queries or queries without history. To alleviate the problem, we provide a solution for the queries regardless of query history. In this paper, we present a new metric using the topic distribution of user documents in the topical user profile, to estimate the potential for personalization for all queries. Using the proposed metric, we can achieve more performance for queries with history and solve the cold start problem of queries without history. To improve personalized search, we provide a personalization ranking model by combining personalized and non-personalized topic models where the proposed metric is used to estimate personalization. The result reveals that the personalization ranking model using the proposed metric improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 5% and 4% respectively.