IMPROVING ENCARTA SEARCH ENGINE PERFORMANCE BY MINING USER LOGS

CHARLES X. LING, JIANFENG GAO, HUAJIE ZHANG, WEINING QIAN, HONGJIANG ZHANG
2002 International journal of pattern recognition and artificial intelligence  
We propose a data-mining approach that produces generalized query patterns (with generalized keywords) from the raw user logs of the Microsoft Encarta search engine (http://encarta.msn.com). Those query patterns can act as cache of the search engine, improving its performance. The cache of the generalized query patterns is more advantageous than the cache of the most frequent user queries since our patterns are generalized, covering more queries and future querieseven those not previously
more » ... Our method is unique since query patterns discovered reflect the actual dynamic usage and user feedbacks of the search engine, rather than the syntactic linkage structure of web pages (as Google does). Simulation shows that such generalized query patterns improve search engine's overall speed considerably. The generalized query patterns, when viewed with a graphical user interface, are also helpful to web editors, who can easily discover topics in which users are mostly interested. Keywords: web log mining, web mining, data mining on the Internet, search engine improvement.
doi:10.1142/s0218001402002179 fatcat:zrwp3rllpvho5itqkfmc4lfrsu