Ranieri Baraglia
Raffaele Perego
Fabrizio Silvestri
ABSTRACT
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.
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Digital Library
- U. Shardanand and P. Maes. Social information filtering: algorithms for automating "word of mouth". In CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 210--217, New York, NY, USA, 1995. ACM Press/Addison-Wesley Publishing Co. Google ScholarDigital Library
- Z. Zhang and O. Nasraoui. Mining search engine query logs for query recommendation. In WWW '06: Proceedings of the 15th international conference on World Wide Web, pages 1039--1040, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
Index Terms
- Search shortcuts: driving users towards their goals
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