VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

TL;DR

This paper introduces VRConvMF, a novel visual recurrent convolutional matrix factorization method that leverages textual and visual features from movie posters and descriptions to improve recommendation accuracy in sparse data scenarios.

Contribution

The paper proposes a new probabilistic matrix factorization approach that integrates multi-level visual features with textual data for enhanced movie recommendations.

Findings

VRConvMF outperforms existing recommendation schemes.

Utilizes visual features from posters to mitigate data sparsity.

Validated on three real-world datasets.

Abstract

Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items. In particular, the visual information of items, such as the movie poster, can be considered as the supplement for item description documents, which helps to obtain more item features. In this paper, we focus on movie recommender system and propose a probabilistic matrix factorization based recommendation scheme called visual recurrent convolutional matrix factorization (VRConvMF), which utilizes the textual and multi-level visual features extracted from the descriptive texts and posters respectively. We implement the proposed VRConvMF and conduct extensive experiments on three commonly used real world datasets to validate its effectiveness. The experimental results illustrate that the proposed VRConvMF outperforms the existing schemes.