论文标题
rankmat：具有校准的分布式嵌入和公平性增强的基质分解
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement
论文作者
论文摘要
矩阵分解是推荐系统领域中广泛采用的技术。矩阵分解技术范围从SVD，LDA，PLSA，SVD ++，MATREC，ZIPF矩阵分解和ITEM2VEC。近年来，分布式单词嵌入激发了推荐系统领域的创新。 Word2Vec和Glove在许多工业应用方案（例如小米的推荐系统）中尤其强调。在本文中，我们提出了一个受幂律和手套理论启发的新矩阵分解。我们利用帕累托分布来对我们的损失函数进行建模，而不是手套模型的指数性质。我们的方法在理论上是可以解释的，在实践中易于实现。在“实验”部分中，我们证明我们的方法优于香草基质分解技术，并且在准确性和公平度量方面都与基于手套的模型相媲美。
Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, distributed word embeddings have inspired innovation in the area of recommender systems. Word2vec and GloVe have been especially emphasized in many industrial application scenario such as Xiaomi's recommender system. In this paper, we propose a new matrix factorization inspired by the theory of power law and GloVe. Instead of the exponential nature of GloVe model, we take advantage of Pareto Distribution to model our loss function. Our method is explainable in theory and easy-to-implement in practice. In the experiment section, we prove our approach is superior to vanilla matrix factorization technique and comparable with GloVe-based model in both accuracy and fairness metrics.
