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Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles’ information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering(CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with “user attitudes”(i.e., abstract rating patterns) over the same distribution, our method achieves greater accuracy than the traditional approach on the rating prediction task. Moreover, with review text information involved, latent user rating attitudes are interpretable and “cold-start” problem can be alleviated.This property qualifies our method for serving as a “recommender” task with very sparse datasets. Furthermore,unlike most related works, we treat each review as a document, not all reviews of each user or item together as one document, to fully exploit the reviews’ information. Experimental results on 25 real-world datasets demonstrate the superiority of our model over state-of-the-art methods.
Intuitively, not only do ratings include include information for learning user preferences, but also for comparison by by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles’ information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering (CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with “ user attitudes ”(ie, abstract rating patterns) over the same distribution, our method achieves greater accuracy than the traditional approach on the rating prediction task. Moreover, with review text information involved, latent user rating attitudes are interpretable and “ cold- start ”problem can be alleviated.This property qualifies our method for serving as a “ recommender ”task with very sparse d atasets. Moreover, unlike most related works, we treat each review as a document, not all reviews of each user or item together as one document, to fully exploit the reviews’ information. our model over state-of-the-art methods.