The effectiveness of factorization and similarity blending

TL;DR

This paper reviews collaborative filtering techniques, demonstrating that blending factorization and similarity models improves recommendation accuracy and introducing a stochastic extension to reduce computational complexity.

Contribution

It presents a novel blending approach for CF models and introduces a stochastic extension of the SCSR similarity model, enhancing performance and efficiency.

Findings

Blending factorization and similarity models reduces error by 9.4%.

The stochastic SCSR model lowers asymptotic complexity.

Blending improves recommendation accuracy over individual models.

Abstract

Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Z\"urich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.