论文标题
基于三重损失的基质矩阵分解以实现强大的建议
Triplet Losses-based Matrix Factorization for Robust Recommendations
论文作者
论文摘要
与其他基于学习的模型一样，推荐系统可能会受到培训数据中偏见的影响。虽然典型的评估指标（例如命中率）并不关心它们，但某些最终用户的某些类别受这些偏见的影响很大。在这项工作中，我们建议使用多个三胞胎损失术语来提取用户和项目的有意义且可靠的表示。我们通过几个“偏见”评估指标以及对训练集变化的稳定和预测方差的一致性来评估此类表示的健全性。每个用户的。
Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representations of users and items. We empirically evaluate the soundness of such representations through several "bias-aware" evaluation metrics, as well as in terms of stability to changes in the training set and agreement of the predictions variance w.r.t. that of each user.
