Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
SELC: a self-supervised model for sentiment classification
Proceedings of the 18th ACM conference on Information and knowledge management
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
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Collaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in some web applications, which has critical impact to the application of CF recommenders. In this article we aim to enhance the online recommender system by fusing virtual ratings as derived from user reviews. Specifically, taking into account of Chinese reviews' characteristics, we propose to fuse the self-supervised emotion-integrated sentiment classification results into CF recommenders, by which the User-Item Rating Matrix can be inferred by decomposing item reviews that users gave to the items. The main advantage of this approach is that it can extend CF recommenders to some web applications without user rating information. In the experiments, we have first identified the self-supervised sentiment classification's higher precision and recall by comparing it with traditional classification methods. Furthermore, the classification results, as behaving as virtual ratings, were incorporated into both user-based and item-based CF algorithms. We have also conducted an experiment to evaluate the proximity between the virtual and real ratings and clarified the effectiveness of the virtual ratings. The experimental results demonstrated the significant impact of virtual ratings on increasing system's recommendation accuracy in different data conditions (i.e., conditions with real ratings and without).