Conference Proceeding Article
Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our system is indeed able to learn both quickly and accurately.
machine learning, recommender system, user feedback, context weight
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
MDM '05: Proceedings: Sixth International Conference on Mobile Data Management: May, 9-13, 2005, Ayia Napa, Cyprus
City or Country
YAP, Ghim-Eng; TAN, Ah-Hwee; and PANG, Hwee Hwa.
Dynamically Optimized Context in Recommender Systems. (2005). MDM '05: Proceedings: Sixth International Conference on Mobile Data Management: May, 9-13, 2005, Ayia Napa, Cyprus. 265-272. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1137
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