Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Search the web x.0: mining and recommending web-mediated processes
Proceedings of the third ACM conference on Recommender systems
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
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Giving suggestions to users of Web-based services is a common practice aimed at enhancing their navigation experience. Major Web Search Engines usually provide "Suggestions" under the form of queries that are, to some extent, related to the current query typed by the user, and the knowledge learned from the past usage of the system. In this work we introduce "Search Shortcuts" as "Successful" queries allowed, in the past, users to satisfy their information needs. Differently from conventional suggestion techniques, our search shortcuts allows to evaluate effectiveness by exploiting a simple train-and-test approach. We have applied several Collaborative Filtering algorithms to this problem, evaluating them on a real query log data. We generate the shortcuts from all user sessions belonging to the testing set, and measure the quality of the shortcuts suggested by considering the similarity between them and the navigational user behavior.