Query Subtopic Mining via Subtractive Initialization of Non-negative Sparse Latent Semantic Analysis

Ambiguous and multifaceted queries widely exist in academic and commercial search engines. Identifying the popular subtopics of queries is an important issue for search engines. In this paper, we propose a novel method to discover the popular subtopics for a given query. Our method first constructs a search behavior tripartite graph based on the search log data. Then, we utilize a subtractive initialized Non-negative Sparse LSA model to mine subtopics from the tripartite graph. The experimental results on two real-world Chinese search logs (i.e., the CADAL search log and the Sogou search log) show that our proposed method can significantly outperform the other comparison methods in term of MAP, α-nDCG and S-recall. We also applied our method into the CADAL digital library to provide a novel faceted book search service which can reduce the users’ efforts in finding books.