Beyond sessions: Exploiting hybrid contextual information for web search

Abstract

It is essential to fully understand user intents for the optimization of downstream tasks such as document ranking and query suggestion in web search. As users tend to submit ambiguous queries, numerous studies utilize contextual information such as query sequence and user clicks for the auxiliary of user intent modeling. Most of these work adopted Recurrent Neural Network (RNN) based frameworks to encode sequential information within a session, which is hard to realize parallel computation. To this end, we plan to adopt attention-based units to generate context-aware representations for elements in sessions. As intra-session contexts are deficient for handling the data sparsity and cold-start problems in session search, we would also attempt to integrate cross-session dependencies by constructing session graphs on the whole corpus to enrich the representation of queries and documents.