Using viewing time to infer user preference in recommender systems

Parsons, J and Ralph, P and Gallagher, K (2004) Using viewing time to infer user preference in recommender systems. In: AAAI Workshop in Semantic Web Personalization (California) - 2004. unknown, N/A.

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Using_Viewing_Time_to_Infer_User_Preference_in_Recommender_Systems.pdf - Accepted Version

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The need for effective technologies to help Web users locate items (information or products) is increasing as the amount of information on the Web grows. Collaborative filtering is one of the most successful techniques for making recommendations; however, most CF-based systems require explicit user ratings and a large quantity of usage history to function effectively. In addition, such systems typically rely on comparing a user to ‘‘similar’’ users encountered before. We develop and evaluate the idea that viewing time is an indicator of preference for attributes of items, and a recommendation system based on this idea. The system uses only the current user’’s navigational data in conjunction with item property data to make recommendations. We also present empirical evidence that the system makes useful recommendations.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: This is a research-in-progress paper. Please check with the author for an updated version.
Departments: Lancaster University Management School > Management Science
ID Code: 47394
Deposited By: ep_importer_pure
Deposited On: 11 Jul 2011 20:16
Refereed?: Yes
Published?: Published
Last Modified: 23 Jun 2019 23:22

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