论文标题
Moviemat：通过矩阵拟合的矩阵分解的上下文感知电影推荐
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting
论文作者
论文摘要
电影推荐系统广泛应用于Netflix和Tubi等商业环境中。经典的推荐模型利用技术，例如协作过滤，学习排名，矩阵分解和深度学习模型，以实现较低的营销费用和更高的收入。但是，电影的观众在不同情况下对同一电影的评分不同。重要的电影观看环境包括受众心情，位置，天气等。但是，诸如张量分解之类的流行技术消耗了不切实际的存储量，从而大大降低了其在现实世界环境中的可行性。在本文中，我们利用了MATMAT框架，该框架通过矩阵拟合来对矩阵进行分解，以构建一种上下文感知的电影推荐系统，该系统优于经典的矩阵分解，并且在公平度量标准中可比。
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.
