A Novel Probabilistic Framework to Broaden the Context in Query Recommendation Systems

摘要

This paper presents a novel probabilistic framework for broadening the notion of context in web search query recommendation systems. In the relevant literature, query suggestion is typically conducted based on past user actions of the current session, mostly related to query submission. Our proposed framework regards user context in a broader way, consisting of a series of further parameters that express it more thoroughly, such as spatial and temporal ones. Therefore, query recommendation is performed herein by considering the appropriateness of each candidate query suggestion, given this broadened context. Experimental evaluation showed that our proposed framework, utilizing spatiotemporal contextual features, is capable to increase query recommendation performance, compared to state-of-art methods such as co-occurence, adjacency and Variable-length Markov Models (VMM). Due to its generic nature, our framework can operate on the basis of further features expressing the user context than the ones studied in the present work, e.g. affect-related, toward further advancing web search query recommendation.