Group-based Personalization Using Topical User Profile

Abstract

Although user profiles are indicative of the user's interests, they can be incomplete to reflect all the user's interests and in more times it is needed to use a group of personalized user profiles to re-rank the returned results by search engines. One of the disadvantages of the personalization based on the user profile is that it is built by considering only the documents that the user has clicked. The set of the clicked documents might be sparse for some users. Data sparsity can be resolved by backing off to the group of users with similar behavior to the user. In this paper, we present a group-based personalization model using topical user-profiles and compare the result of the proposed ranking methods based on the group and user profiles. To cluster the groups of users we use the Kmeans clustering algorithm and the similarity between users is measured by symmetric Kullback-Leibler divergence between their latent topic distributions. Using the proposed group-based personalization model, we can improve the ranking result using group-based profiles and solve the cold start problem of users without history. In the issues related to privacy concerns, group profiles are also more secure than user profiles because both the computation and the storage of the user information are done as a group of users. The result reveals that the group-based personalization using topical user profile improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 7% and 6% respectively in all short, long and session term profiles while the short term user profile obtains more effective than the others profiles.