Abstract
Latent factor models have become a workhorse for a large number of recommender systems. While these systems 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 pairwise 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 and a proprietary television viewership data set. A user study and automated experiments validate our findings.
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Suhrid Balakrishnan is a researcher in AT&T Labs, specializing in machine learning, particularly interested in accurate and efficient algorithms for statistical learning. He obtained his PhD in Computer Science from Rutgers University in 2007, and has been at AT&T Labs Research since he graduated. Suhrid is particularly interested in predictive modelling for recommender systems and computational advertising, and in general is motivated by the application of machine learning to real-world problems from domains like telecommunications, language, and biology.
Sumit Chopra received his PhD in Computer Science in 2008, from Courant Institute of Mathematical Sciences, at New York University, under the supervision of Prof. Yann Le-Cun. He is since a Senior Member of Technical Staff in the Statistics group at AT&T Labs-Research. His primary area of interest is machine learning and pattern recognition, with a focus towards automatic task specific feature-learning using multilayer non-linear connectionist architectures. His research has resulted in the development of efficient feature learning models, which can handle the uncertainties and interdependencies among samples in large scale data sets. These models have been applied to a variety of domains, such as, computer vision, robotics, economics, natural language processing, automatic speech recognition, and computational advertising.
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Balakrishnan, S., Chopra, S. Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models. Front. Comput. Sci. 6, 197–208 (2012). https://doi.org/10.1007/s11704-012-2871-7
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DOI: https://doi.org/10.1007/s11704-012-2871-7