Effective Personalized Search With Heterogeneous Graph Based Hawkes Process

Personalized search aims at re-ranking search results with reference to users' background information. The state-of-the-art personalized search methods often consider both the short-term search interests from current session behaviors and the long-term search interests from previous session behaviors. However, sessions in real-world search scenarios are usually very short, and a large number of sessions contain only one query, which makes it difficult to model short-term search interests. Intuitively, apart from current session behaviors, some recent historical session behaviors could also contribute to the current search interests, and the influence of these behaviors typically decays over time. Based on this intuition, we propose a novel heterogeneous graph based Hawkes process to improve the effectiveness of personalized search. Specifically, we first construct a heterogeneous graph to model multiple relations between users, queries, and documents. Then, we propose a heterogeneous graph neural network based algorithm to encode the representations of users' historical search behaviors. After that, we develop a multivariate Hawkes process to capture the influence of historical search behaviors on the current search intent. Our approach can dynamically model the influence of historical behaviors in a continuous time space. Thus, both the current session behaviors and the historical session behaviors can be utilized to characterize a more accurate current search intent. We evaluate our method using three real-life datasets, and the results show that our approach significantly outperforms the state-of-the-art methods in terms of several widely-used precision metrics.