Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
AT'13 Proceedings of the Second international conference on Agreement Technologies
From blurry numbers to clear preferences: A mechanism to extract reputation in social networks
Expert Systems with Applications: An International Journal
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While latent factor models are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning (or refining) user parameters from such feedback. We propose a scheme where users are presented with a sequence of pair wise preference questions: "Do you prefer item A over B?". User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporating the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain criterion. We validate the scheme on the Netflix movie ratings data set. A user study and automated experiments validate our findings.