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Context-Aware Rank-Oriented Recommender Systems

Abstract Recommender systems are a type of information filtering system that suggests items that may be of interest to a user. Most information retrieval systems have an overwhelmingly large number of entries. Most users would experience information overload if they were forced to explore the full set of results. The goal of recommender systems is to overcome this limitation by predicting how users will value certain items and returning the items that should be of the highest interest to the user. Most recommender systems collect explicit user feedback, such as a rating, and attempt to optimize their model to this rating value. However, there is potential for a system to collect implicit user feedback, such as user purchases and clicks, to learn use... (more)
Created Date 2012
Contributor Ackerman, Brian (Author) / Chen, Yi (Advisor) / Candan, Kasim (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Information science / context-aware / implicit feedback / learning to rank / recommender systems
Type Masters Thesis
Extent 143 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis