Elements of information theory
Elements of information theory
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Robust Stochastic Approximation Approach to Stochastic Programming
SIAM Journal on Optimization
The Journal of Machine Learning Research
Using external aggregate ratings for improving individual recommendations
ACM Transactions on the Web (TWEB)
Recommender Systems Handbook
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Fair and balanced: learning to present news stories
Proceedings of the fifth ACM international conference on Web search and data mining
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
Estimating the prevalence of deception in online review communities
Proceedings of the 21st international conference on World Wide Web
The groupon effect on yelp ratings: a root cause analysis
Proceedings of the 13th ACM Conference on Electronic Commerce
Sequential and Temporal Dynamics of Online Opinion
Marketing Science
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Proceedings of the 22nd international conference on World Wide Web
CoBaFi: collaborative bayesian filtering
Proceedings of the 23rd international conference on World wide web
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Consumer review sites and recommender systems typically rely on a large volume of user-contributed ratings, which makes rating acquisition an essential component in the design of such systems. User ratings are then summarized to provide an aggregate score representing a popular evaluation of an item. An inherent problem in such summarization is potential bias due to raters self-selection and heterogeneity in terms of experience, tastes and rating scale interpretation. There are two major approaches to collecting ratings, which have different advantages and disadvantages. One is to allow a large number of volunteers to choose and rate items directly (a method employed by e.g. Yelp and Google Places). Alternatively, a panel of raters may be maintained and invited to rate a predefined set of items at regular intervals (such as in Zagat Survey). The latter approach arguably results in more consistent reviews and reduced selection bias, however, at the expense of much smaller coverage (fewer rated items). In this paper, we examine the two different approaches to collecting user ratings of restaurants and explore the question of whether it is possible to reconcile them. Specifically, we study the problem of inferring the more calibrated Zagat Survey ratings (which we dub 'expert ratings') from the user-generated ratings ('grassroots') in Google Places. To that effect, we employ latent factor models and provide a probabilistic treatment of the ordinal rankings. We can predict Zagat Survey ratings accurately from ad hoc user-generated ratings by joint optimization on two datasets. We analyze the resulting model, and find that users become more discerning as they submit more ratings. We also describe an approach towards cross-city recommendations, answering questions such as 'What is the equivalent of the Per Se restaurant in Chicago'?