{"type":"ScholarlyArticle","title":"Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness","authors":[{"name":"Kaisong Song","slug":"kaisong-song","given":"Kaisong","family":"Song","affiliation":"Northeastern University , Shenyang , China"},{"name":"Wei Gao","slug":"wei-gao","given":"Wei","family":"Gao","affiliation":"Victoria University of Wellington , New Zealand; Qatar Comput- ing Research Institute; {MoE Key Laboratory on High Con- fidence Software Technologies , China} ,"},{"name":"Shi Feng","slug":"shi-feng","given":"Shi","family":"Feng","affiliation":"Northeastern University , Shenyang , China"},{"name":"Daling Wang","slug":"daling-wang","given":"Daling","family":"Wang","affiliation":"Northeastern University , Shenyang , China"},{"name":"Kam-Fai Wong","slug":"kam-fai-wong","given":"Kam-Fai","family":"Wong","affiliation":"The Chinese University of Hong Kong , Hong Kong"},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":"University of Technology Sydney , Australia"}],"abstract":"Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA).  Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.","doi":"10.24963/ijcai.2017/382","url":"https://ijcai.org/proceedings/2017/382","pdf":"https://ijcai.org/proceedings/2017/0382.pdf","markdown":"https://ijcai.org/proceedings/2017/382.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/382","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":382,"pages":{"first":2744,"last":2750},"keywords":["Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Personalization and User Modeling","Natural Language Processing: Sentiment Analysis and Text Mining","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2011] Deepak Agarwal, Bee-Chung Chen, and Bo Pang. 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