A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models

نویسندگان

  • Ammar Alanazi
  • Michael Bain
چکیده

Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and use that representation in creating a reciprocal recommender system applied to online dating. In this paper, we develop a general framework for combining a Hidden Markov model (HMM) content-based reciprocal recommender system with collaborative filtering techniques to create a unified hybrid recommender. Additionally, a new similarity measure is introduced to rank the recommendations generated by this hybrid recommender. Moreover, we propose, design and implement a reciprocal recommender system using the suggested framework and the new similarity measure. Evaluation of this system shows that it generates better recommendations than existing systems in a time-efficient manner.

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تاریخ انتشار 2016