How do users describe their information need: Query recommendation based on snippet click model

Query recommendation helps users to describe their information needs more clearly so that search engines can return appropriate answers and meet their needs. State-of-the-art researches prove that the use of users’ behavior information helps to improve query recommendation performance. Instead of finding the most similar terms previous users queried, we focus on how to detect users’ actual information need based on their search behaviors. The key idea of this paper is that although the clicked documents are not always relevant to users’ queries, the snippets which lead them to the click most probably meet their information needs. Based on analysis into large-scale practical search behavior log data, two snippet click behavior models are constructed and corresponding query recommendation algorithms are proposed. Experimental results based on two widely-used commercial search engines’ click-through data prove that the proposed algorithms outperform practical recommendation methods of these two search engines. To the best of our knowledge, this is the first time that snippet click models are proposed for query recommendation task.

► Users’ information needs are not only expressed by their search queries, but also by the snippets of the results they click. ► A query recommendation framework was proposed in which keywords are recommended based on their appearance in clicked snippets. ► The nature of query recommendation process was analyzed from user’s perspective. Two snippet click models and corresponding algorithms are presented. ► Effectiveness of the proposed query recommendation method was demonstrated based on two commercial search engine’s click-through logs.