Effective Pre-rating Method Based on Users' Dichotomous Preferences and Average Ratings Fusion for Recommender Systems
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초록
With an increase in the scale of recommender systems, users’ rating data tend to be extremely sparse. Somemethods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users’ dichotomous preferences and averageratings fusion. First, based on a user–item ratings matrix, a new user-item preference matrix was constructedto analyze and model user preferences. The items were then divided into two categories based on aparameterized dynamic threshold. The missing ratings for items that the user was not interested in were directlyfilled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, anoptimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standarddataset. The experimental results show that our method can effectively reduce the prediction error and improvethe recommendation quality. As for its application, our method is effective, but not complicated.
Abstract
With an increase in the scale of recommender systems, users’ rating data tend to be extremely sparse. Somemethods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users’ dichotomous preferences and averageratings fusion. First, based on a user–item ratings matrix, a new user-item preference matrix was constructedto analyze and model user preferences. The items were then divided into two categories based on aparameterized dynamic threshold. The missing ratings for items that the user was not interested in were directlyfilled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, anoptimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standarddataset. The experimental results show that our method can effectively reduce the prediction error and improvethe recommendation quality. As for its application, our method is effective, but not complicated.
참고문헌 (12)
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