# VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

arXiv: https://arxiv.org/abs/2202.10241 · HTML: https://mlsanity.com/paper/2202.10241/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Random Walks: A Review of Algorithms and Applications](https://mlsanity.com/paper/2008.03639/) (2020)
- [Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences](https://mlsanity.com/paper/2008.04652/) (2020)
- [A Deep Multimodal Approach for Cold-start Music Recommendation](https://mlsanity.com/paper/1706.09739/) (2017)

## Built on by (most cited first, 1 of 1)

- [Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach](https://mlsanity.com/paper/2204.11602/) (2022)

## Abstract (arXiv)

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.