Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Machine Learning
Feature Generation Using General Constructor Functions
Machine Learning
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
Recommendation as link prediction: a graph kernel-based machine learning approach
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Content-based recommendation systems
The adaptive web
Generation of attributes for learning algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Cross-technique mediation of user models
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
The groupon effect on yelp ratings: a root cause analysis
Proceedings of the 13th ACM Conference on Electronic Commerce
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Why people use Yelp.com: An exploration of uses and gratifications
Computers in Human Behavior
Cross social networks interests predictions based ongraph features
Proceedings of the 7th ACM conference on Recommender systems
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Many types of recommender systems rely on a rich ensemble of user, item, and context features when generating recommendations for users. The features can be either manually engineered or automatically extracted from the available data, such that feature engineering becomes an important step in the recommendation process. In this work, we propose to leverage graph based representation of the data in order to generate and automatically populate features. We represent the standard user-item rating matrix and some domain metadata, as graph vertices and edges. Then, we apply a suite of graph theory and network analysis metrics to the graph based data representation, to populate features that augment the original user-item ratings data. The augmented data is fed into a classifier that predicts unknown user ratings, which are used for the generation of recommendations. We evaluate the proposed methodology using the recently released Yelp business ratings dataset. Our results indicate that the automatically populated graph features allow for more accurate and robust predictions, with respect to both the variability and sparsity of ratings.