Personalized document re-ranking based on Bayesian probabilistic matrix factorization

A query considered in isolation provides limited information about the searcher's interest. Previous work has considered various types of user behavior, e.g., clicks and dwell time, to obtain a better understanding of the user's intent. We consider the searcher's search and page view history. Using search logs from a commercial search engine, we (i) investigate the impact of features derived from user behavior on reranking a generic ranked list; (ii) optimally integrate the contributions of user behavior and candidate documents by learning their relative importance per query based on similar users. We use dwell time on clicked URLs when estimating the relevance of documents for a query, and perform Bayesian Probabilistic Matrix Factorization as smoothing to predict the relevance. Considering user behavior achieves better rankings than non-personalized rankings. Aggregation of user behavior and query-document features with a user-dependent adaptive weight outperforms combinations with a fixed uniform value.