Query suggestion with diversification and personalization

Web search query suggestion is an important functionality that facilitates information seeking of search engine users. In existing work, the concepts of diversification and personalization have been individually introduced to query suggestion systems. In this paper, we propose a new query suggestion paradigm, Query Suggestion With Diversification and Personalization (QS-DP) to effectively integrate diversification and personalization into one unified framework. In the QS-DP, the suggested queries are effectively diversified to cover different facets of the input query while the ranking of the suggested queries are personalized to ensure that the top ones are those that align with a user’s personal preferences. We evaluate QS-DP on a commercial search engine query log against several existing query suggestion methods. The experimental results verify our hypothesis that diversification and personalization can be effectively integrated and they are able to enhance each other within the QS-DP framework, which significantly outperforms several strong baselines with respect to a series of metrics.