An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning to recommend helpful hotel reviews
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Communications of the ACM
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
mTrust: discerning multi-faceted trust in a connected world
Proceedings of the fifth ACM international conference on Web search and data mining
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
eTrust: understanding trust evolution in an online world
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.