Content type based adaptation in collaborative recommendation

초록

"In this paper, we propose an adaptive and collaborative recommendation method based on c ontent type, which can enhance performance considerably in practice. Conventional collaborative recommendations are troubled with no or little effective rating information for newly comers or even some old users so that they often work poorly. In order to relax such cold start or sparse rating information problems, we employ a user-content type matrix with relatively higher density than commonly-used user-content matrix. By using user-content type matrix, we evaluate user's preference for a content type and then reflect it to the final prediction of content preference in collaborative recommendation. In such a way, our method adaptively combines content preference with content type preference. I n experiments, we identify notable performance improvement compared to traditional collaborative recommendation methods in terms of MAE (Mean Absolute Error) and coverage.

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