One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems

29 Jun 2019Fakhri AbbasXi Niu

Today's recommender systems are criticized for recommending items that are too obvious to arouse users' interest. That's why the recommender systems research community has advocated some "beyond accuracy" evaluation metrics such as novelty, diversity, coverage, and serendipity with the hope of promoting information discovery and sustain users' interest over a long period of time... (read more)

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