Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and deep learning models to achieve lower marketing expenses and higher revenues. However, audience of movies have different ratings of the same movie in different contexts. Important movie watching contexts include audience mood, location, weather, etc. Tobe able to take advantage of contextual information is of great benefit to recommender builders. However, popular techniques such as tensor factorization consumes an impractical amount of storage, which greatly reduces its feasibility in real world environment. In this paper, we take advantage of the MatMat framework, which factorizes matrices by matrix fitting to build a context-aware movie recommender system that is superior to classic matrix factorization and comparable in the fairness metric.

翻译：电影建议系统在NetFlix和Tubi等商业环境中广泛应用。经典建议模型利用合作过滤、学习排名、矩阵系数化和深层次学习模型等技术来实现较低的营销开支和更高的收入。然而,电影观众在不同情况下对同一电影的评分不同。重要的电影观察环境包括观众的情绪、位置、天气等。能够利用背景信息对推荐人大有裨益。然而,推理等流行技术消耗了不切实际的存储量,大大降低了其在现实世界环境中的可行性。在本论文中,我们利用MatMat框架,通过配置矩阵将矩阵作为推算因素,以构建一种符合背景的电影建议系统,该系统优于典型的矩阵系数化和公平度指标的可比性。