论文标题
MATREC：高度偏斜数据集的矩阵分解
MatRec: Matrix Factorization for Highly Skewed Dataset
论文作者
论文摘要
推荐系统是在互联网合作中应用的最成功的AI技术之一。 Tiktok，Amazon和YouTube等流行的Internet产品将全部集成的推荐系统作为其核心产品功能。尽管推荐系统取得了巨大的成功，但它以高度偏斜的数据集而闻名，工程师和研究人员需要调整其方法以解决特定问题以产生良好的结果。无法处理高度偏斜的数据集通常会为大数据集群产生严重的计算问题，并且为客户提供了不令人满意的结果。在本文中，我们提出了一种新算法，该算法解决了矩阵分解框架中的问题。我们在方法的理论建模中使用易于解释和易于实施的公式建模数据偏度因子。我们在实验中证明，我们的方法通过流行的推荐系统算法（例如学习排名，交替的最小二乘和深度矩阵分解）产生了相当喜欢的结果。
Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature. Although recommender systems have received great success, it is well known for highly skewed datasets, engineers and researchers need to adjust their methods to tackle the specific problem to yield good results. Inability to deal with highly skewed dataset usually generates hard computational problems for big data clusters and unsatisfactory results for customers. In this paper, we propose a new algorithm solving the problem in the framework of matrix factorization. We model the data skewness factors in the theoretic modeling of the approach with easy to interpret and easy to implement formulas. We prove in experiments our method generates comparably favorite results with popular recommender system algorithms such as Learning to Rank , Alternating Least Squares and Deep Matrix Factorization.
