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• W2050096199 abstract "Most existing recommender systems focus on modeling the ratings while ignoring the abundant information embedded in the review text. In this paper, we propose a unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews. We apply topic modeling techniques on the review text and align the topics with rating dimensions to improve prediction accuracy. With the information embedded in the review text, we can alleviate the cold-start problem. Furthermore, our model is able to learn latent topics that are interpretable. With these interpretable topics, we can explore the prior knowledge on items or users and recommend completely cold' items. Empirical study on 27 classes of real-life datasets show that our proposed model lead to significant improvement compared with strong baseline methods, especially for datasets which are extremely sparse where rating-only methods cannot make accurate predictions." @default.
• W2050096199 created "2016-06-24" @default.
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• W2050096199 date "2014-10-06" @default.
• W2050096199 modified "2023-10-17" @default.
• W2050096199 title "Ratings meet reviews, a combined approach to recommend" @default.
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• W2050096199 doi "https://doi.org/10.1145/2645710.2645728" @default.
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