Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Enhancing a digital book with a reading recommender
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
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Recommender systems have proven to be a valuable tool for web companies like Amazon and Netflix for attracting and maintaining a large user base. However, in situations when user data is more scarce (e.g., for mid-sized companies, or an online retailer testing a new ratings system) algorithms tailored to smaller datasets can be used to further increase accuracy. This paper explores the potential of combining collaborative and content-based (using user comments) filtering algorithms using Yelp.com data from a single city. We present the method to combine two approaches, and find that the MSE for predicting a user's new rating can be reduced from a baseline MSE of 1.744 to 0.934 given just 2500 rated items in our real-world dataset.