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Inf. Syst."],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>\n            Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users\u2019 preferences and items\u2019 features from different\n            <jats:italic>aspects<\/jats:italic>\n            , and also estimate the\n            <jats:italic>aspect importance<\/jats:italic>\n            of a user toward an item. Then, the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user\u2019s and item\u2019s latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user\u2019s preferences and the targeted item\u2019s features. Therefore, it is expected that the proposed method can model a user\u2019s preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance\u2014the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail.\n          <\/jats:p>","DOI":"10.1145\/3291060","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T13:32:12Z","timestamp":1547213532000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":212,"title":["MMALFM"],"prefix":"10.1145","volume":"37","author":[{"given":"Zhiyong","family":"Cheng","sequence":"first","affiliation":[{"name":"Qilu University of Technology (Shandong Academy of Sciences), China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Chang","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shandong Normal University, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rose C.","family":"Kanjirathinkal","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohan","family":"Kankanhalli","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,1,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13657-3_43"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence","volume":"14","author":"Bao Yang","year":"2014","unstructured":"Yang Bao , Hui Fang , and Jie Zhang . 2014 . TopicMF: Simultaneously exploiting ratings and reviews for recommendation . In Proceedings of the 28th AAAI Conference on Artificial Intelligence , Vol. 14 . 2--8. Yang Bao, Hui Fang, and Jie Zhang. 2014. TopicMF: Simultaneously exploiting ratings and reviews for recommendation. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, Vol. 14. 2--8."},{"key":"e_1_2_1_3_1","article-title":"Matching words and pictures","author":"Barnard Kobus","year":"2003","unstructured":"Kobus Barnard , Pinar Duygulu , David Forsyth , Nando de Freitas , David M. Blei , and Michael I. Jordan . 2003 . Matching words and pictures . Journal of Machine Learning Research 3 , ( Feb. 2003), 1107--1135. Kobus Barnard, Pinar Duygulu, David Forsyth, Nando de Freitas, David M. Blei, and Michael I. Jordan. 2003. Matching words and pictures. 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