Mining User Preferences
The interactions between the user and an information access system can be analyzed and studied to gather user preferences and to learn what the user likes the most, and to use this information to personalize the presentation of results. Computer science researchers have been building a case for search log access to allow them study and analyze new algorithms via a common benchmark search log, as well as to learn about user information needs and query formulation approaches. Social scientists could investigate the use of language in queries as well as discrepancies between user interests as revealed by their queries versus user interests as revealed by face-to-face surveys. Advertisers could use interaction logs to understand how users navigate to their pages, gain a better understanding of their competitors, and improve keyword advertising campaigns.
The LogCLEF 2011 lab - "A benchmarking activity on Multilingual Log File Analysis: Language identification, query classification, success of a query" deals with information contained in query logs of search engines and digital libraries from which knowledge can be mined to understand search behavior in multilingual context. The lab will also support the community by sharing resources and knowledge on log analysis such as annotated data, open source code, documents and articles, which can serve as gold standards or training data for future evaluations.