Recommender system utilizing collaborative filtering combining explicit and implicit feedback with both neighborhood and latent factor models

Abstract

Example collaborative filtering techniques provide improved recommendation prediction accuracy by capitalizing on the advantages of both neighborhood and latent factor approaches. One example collaborative filtering technique is based on an optimization framework that allows smooth integration of a neighborhood model with latent factor models, and which provides for the inclusion of implicit user feedback. A disclosed example Singular Value Decomposition (SVD)-based latent factor model facilitates the explanation or disclosure of the reasoning behind recommendations. Another example collaborative filtering model integrates neighborhood modeling and SVD-based latent factor modeling into a single modeling framework. These collaborative filtering techniques can be advantageously deployed in, for example, a multimedia content distribution system of a networked service provider.