Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user’s taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of noise that challenges the validity of this assumption. In this project, we tackle the problem of analyzing and characterizing the noise in user feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We measure RMSE values that range from 0.557 to 0.8156.We also analyze how factors such as item sorting and time of rating affect this noise.
Fig 1. User Interface used in our user study
“I Like It, I Like It Not” , Amatriain, X., Pujol, J.M. and Oliver, N. (2009), Proceedings Int. Conf. on User Modeling and Adaptive Hypermedia (UMAP’09), Trento, Italy, June 2009
Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. How- ever, this approach suffers from several shortcomings, in- cluding data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for rec- ommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of ex- pert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in tradi- tional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Fi- nally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.
“The Wisdom of the Few: A Collaborative Filtering Approach Based on Expert Opinions from the Web” , Amatriain, X., Lathia, N., Pujol, J.M., Hwak, H. and Oliver, N. (2009), Proceedings Int. Conf. on Information Retrieval(SIGIR’09), Boston, July 19-23, 2009