Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

Abstract

While collaborative filtering (CF) is the most popular approach for recommendation systems, it only makes use of the ratings given to items by users and neglects side information about user attributes or item features. In this work, a natural language processing (NLP) technique is applied to generate a more consistent version of Tag Genome, a side information which is associated with each movie in the MovieLens 20M dataset. Subsequently, we propose a 3-layer autoencoder to create a more compact representation of these tags which improves the performance of the system both in accuracy and in computational complexity. Finally, the proposed representation and the well-known matrix factorization techniques are combined into a unified framework that outperforms the state-of-the-art models by at least 2.87% and 3.36% in terms of RMSE and MAE, respectively.

Keywords

• Collaborative filtering
• matrix factorization
• neighborhood-based
• recommendation system
• similarity measure