Mining long-lasting exploratory user interests from search history
Bin Tan, Yuanhua Lv, ChengXiang Zhai · 2012
A user's web search history contains many valuable search patterns. In this paper, we study search patterns that represent a user's long-lasting and exploratory search interests. By focusing on long-lastingness and exploratoriness, we are able to discover search patterns that are most useful for recommending new and relevant information to the user. Our approach is based on language modeling and clustering, and specifically designed to handle web search logs. We run our algorithm on a real web search log collection, and evaluate its performance using a novel simulated study on the same search log dataset. Experiment results support our hypothesis that long-lastingness and exploratoriness are necessary for generating successful recommendation. Our algorithm is shown to effectively discover such search interest patterns, and thus directly useful for making recommendation based on personal search history.