Personalizedery Suggestion Diversification

ery suggestions help users re€ne their queries a‰er they input an initial query. We consider the task of generating query suggestions that are personalized and diversi€ed. We propose a personalized query suggestion diversi€cation model (PQSD), where a user’s long-term search behavior is injected into a basic greedy query suggestion diversi€cation model (G-QSD) that considers a user’s search context in their current session. ery aspects are identi€ed through clicked documents based on the Open Directory Project (ODP). We quantify the improvement of PQSD over a state-of-theart baseline using the AOL query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversi€cation. ‘e experimental results show that PQSD achieves the best performance when only queries with clicked documents are taken as search context rather than all queries. ACM Reference format: Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2017. Personalizedery Suggestion Diversi€cation. In Proceedings of SIGIR ’17, Shinjuku, Tokyo, Japan, August 07-11, 2017, 4 pages. DOI: h‹p://dx.doi.org/10.1145/3077136.3080652