关键词: 查询推荐, 查询流图, 搜索状态, 满意度

Abstract: The traditional query recommendation algorithms generate query suggestions by mining query logs of search engines. However, existing models focus more on the relationships (e.g., semantic similarity or relevance etc.) between original query and candidate suggestions without considering users’ search state during the search process. To address this challenge, this paper proposes a basic assumption for query suggestion inspired by the fact that users have different satisfaction states when searching for information, then verifies the assumption by conducting a large scale online user questionnaire. According to the verified assumption, this paper presents an auto-adaptive query recommendation model which is able to provide different categories of queries to users intelligently. In the proposed model, the system will provide new query suggestions to users when they are satisfied with current search results; on the contrary, the system will tend to recommend those query suggestions which are relevant and have more powerful representing ability. Large scale experiments on query logs show that the proposed model outperforms the traditional query flow graph model significantly and demonstrate the effectiveness of the proposed model.

Key words: query recommendation, query flow graph, search state, satisfaction