Query-URL Bipartite Based Approach to Personalized Query Recommendation

Query recommendation is considered an effective assistant in enhancing keyword based queries in search engines and Web search software. Conventional approach to query recommendation has been focused on query-term based analysis over the user access logs. In this paper, we argue that utilizing the connectivity of a query-URL bipartite graph to recommend relevant queries can significantly improve the accuracy and effectiveness of the conventional query-term based query recommendation systems. We refer to the Query-URL Bipartite based query reCommendation approach as QUBIC. The QUBIC approach has two unique characteristics. First, instead of operating on the original bipartite graph directly using biclique based approach or graph clustering, we extract an affinity graph of queries from the initial query-URL bipartite graph. The affinity graph consists of only queries as its vertices and its edges are weighted according to a queryURL vector based similarity (distance) measure. By utilizin...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Lin Li, Zhenglu Yang, Ling Liu, Masaru Kitsuregawa
• Keywords: AAAI 2008, Affinity Graph, Bipartite Graph, Intelligent Agents, Query Recommendation