VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation
This work addresses sparsity in recommender systems for movie platforms by incorporating visual data, representing an incremental improvement over prior methods.
The paper tackles the sparsity problem in movie recommendation systems by proposing VRConvMF, a method that integrates textual and multi-level visual features from posters and descriptions into probabilistic matrix factorization, and shows it outperforms existing schemes on three real-world datasets.
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.