Dynamically Optimized Context in Recommender Systems

• Main Authors: YAP, Ghim-Eng, TAN, Ah-Hwee, PANG, Hwee Hwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: machine learning; recommender system; user feedback; context weight; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1137; https://ink.library.smu.edu.sg/context/sis_research/article/2136/viewcontent/Dynamically_Optimized_Context_in_Recommender_Systems__edited_.pdf
• Institution: Singapore Management University

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.