Predicting a Web Search Engine User''s Future Behavior using Query Log

• Main Authors: Hsin-Yih Lin, 林欣毅
• Other Authors: Hsin-Hsi Chen
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/13452899274208826270

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 99 === In this study, we devise methods to predict the future search action (i.e., query or clicked URL) sequence of a search engine user. These predictions have the potential application of being integrated into a web search engine to facilitate a search engine user’s search process. The corpus used is the Microsoft query log dataset containing search sessions in 2006. The corpus is divided into a training dataset and a test dataset for our machine-learning methods. We propose four methods: WTAL, SRPF, SRPP and ACTF. WTAL is based on the concept of the co-occurrence relationship between past queries and clicked URLs. Both SRPF and SRPP incorporate information retrieval methodologies into their algorithms. ACTF is a graph-based method employing PageRank. We further merge several of our methods together. Experimental results show that WTAL has the best individual method performance. However, the combination of the methods together outperforms individual methods. Additional analysis reveals that using every query and URL that a user has already submitted or clicked on in a session does not necessarily produce a better prediction performance than using only the user’s most recent actions. It is also found that it is harder to predict future queries and clicked URLs for sessions with more queries than for sessions with fewer queries.