The effectiveness of factorization and similarity blending
This work addresses recommendation systems for users, but it is incremental as it builds on existing techniques.
The authors tackled the problem of improving collaborative filtering for recommendations by blending factorization-based and similarity-based approaches, resulting in a 9.4% error decrease compared to the best stand-alone model.
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 Zürich. 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.