Personalized query suggestion diversification in information retrieval

Abstract

Query suggestions help users refine their queries after they input an initial query. Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based, developing models that either focus on adapting to a specific user (personalization) or on diversifying query aspects in order to maximize the probability of the user being satisfied (diversification). We consider the task of generating query suggestions that are both personalized and diversified. We propose a personalized query suggestion diversification (PQSD) model, where a user’s long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user’s search context in their current session. Query aspects are identified through clicked documents based on the open directory project (ODP) with a latent dirichlet allocation (LDA) topic model. We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online (AOL) query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification. The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries, especially when more query suggestions are returned in the list.