Abstract:

Recommender systems provide their users an efficient way to handle information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multicriteria recommender systems are an extension of traditional recommender systems that utilize multicriteria-based user preferences. Multicriteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multicriteria recommender systems may fail to find similar neighbors to an active user due to the lack of corated items among users. Therefore, we propose a new similarity-based multicriteria collaborative filtering approach based on autoencoders. In order to handle sparsity, the proposed method extracts nonlinear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multicriteria recommendation techniques.

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