Dynamically-optimized context in recommender systems

  • Authors:
  • Ghim-Eng Yap;Ah-Hwee Tan;Hwee-Hwa Pang

  • Affiliations:
  • Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Institute for Infocomm Research, Singapore

  • Venue:
  • Proceedings of the 6th international conference on Mobile data management
  • Year:
  • 2005

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Abstract

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.