Factorization meets the neighborhood

TLDR

Recommender systems personalize product or service suggestions, typically using collaborative filtering implemented via latent factor models or neighborhood models. This study introduces innovations to both latent factor and neighborhood collaborative filtering approaches. The authors evaluate the proposed methods on the Netflix dataset and propose a new top‑K recommendation metric. Merging factor and neighborhood models and incorporating explicit and implicit feedback yields a more accurate combined model, outperforming prior results on Netflix.

Abstract

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.