Learning A Semantic Space Of Web Search Via Session Data

In Web search, a user first comes up with an information need and issues an initial query. Then some retrieved URLs are clicked and other queries are issued if he/she is not satisfied. We advocate that Web search is governed by a hidden semantic space, and each involved element such as query and URL has its projection, i.e., as a vector, in this space. Each of above actions in the search procedure, i.e. issuing queries or clicking URLs, is an interaction result of those elements in the space. In this paper, we aim at uncovering such a semantic space of Web search that uniformly captures the hidden semantics of search queries, URLs and other elements. We propose session2vec and session2vec+ models to learn vectors in the space with search session data, where a search session is regarded as an instantiation of an information need and keeps the interaction information of queries and URLs. Vector learning is done on a large query log from a search engine, and the efficacy of learnt vectors is examined in a few tasks.