Encoding Group Interests With Persistent Homology for Personalized Search

Personalized search aims to customize search results based on users' search history. The key of personalized search is to learn the representations of users' interests from users' search history. The state-of-the-art personalized search methods often encode group-level features of similar users to improve personalized search. However, existing group-level feature encoding methods are sensitive to noisy users, which are often contained in real-world search data. To overcome this problem, we propose a novel approach to encode group features based on a topological data analysis technique, namely, persistent homology analysis. Such topological features are typically robust to noisy data, thus can improve the personalized search quality. To the best of our knowledge, we are the first to use topological features for improving personalized Web search. We conduct extensive experiments on two real-life datasets to evaluate the proposed approach; and the results show that our solution is significantly better than the state-of-the-art personalized search models in terms of several widely used precision measures.