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Effective metric learning with co-occurrence embedding for collaborative recommendations.
Neural Networks ( IF 6.0 ) Pub Date : 2020-01-30 , DOI: 10.1016/j.neunet.2020.01.021
Hao Wu 1 , Qimin Zhou 1 , Rencan Nie 1 , Jinde Cao 2
Affiliation  

In recommender systems, matrix factorization and its variants have grown up to be dominant in collaborative filtering due to their simplicity and effectiveness. In matrix factorization based methods, dot product which is actually used as a measure of distance from users to items, does not satisfy the inequality property, and thus may fail to capture the inner grained preference information and further limits the performance of recommendations. Metric learning produces distance functions that capture the essential relationships among rating data and has been successfully explored in collaborative recommendations. However, without the global statistical information of user-user pairs and item-item pairs, it makes the model easy to achieve a suboptimal metric. For this, we present a co-occurrence embedding regularized metric learning model (CRML) for collaborative recommendations. We consider the optimization problem as a multi-task learning problem which includes optimizing a primary task of metric learning and two auxiliary tasks of representation learning. In particular, we develop an effective approach for learning the embedding representations of both users and items, and then exploit the strategy of soft parameter sharing to optimize the model parameters. Empirical experiments on four datasets demonstrate that the CRML model can enhance the naive metric learning model and significantly outperforms the state-of-the-art methods in terms of accuracy of collaborative recommendations.

中文翻译:

有效的量度学习,并发嵌入以进行协作推荐。

在推荐系统中,矩阵因式分解及其变体由于其简单性和有效性,已成长为协作过滤的主导。在基于矩阵分解的方法中,实际上用作用户到项之间距离的度量的点积不满足不等式属性,因此可能无法捕获内部粒度的偏好信息,从而进一步限制了推荐的性能。度量学习产生距离函数,该函数捕获评分数据之间的基本关系,并且已在协作推荐中成功进行了探索。但是,由于没有用户-用户对和项目-项目对的全局统计信息,因此该模型易于实现次优度量。为了这,我们为协作推荐提出了一种共现嵌入正则化度量学习模型(CRML)。我们将优化问题视为多任务学习问题,其中包括优化度量学习的主要任务和表示学习的两个辅助任务。特别是,我们开发了一种有效的方法来学习用户和项目的嵌入表示,然后利用软参数共享策略优化模型参数。在四个数据集上的经验实验表明,CRML模型可以增强幼稚的度量学习模型,并且在协作推荐的准确性方面明显优于最新方法。我们认为优化问题是一个多任务学习问题,其中包括优化度量学习的主要任务和表示学习的两个辅助任务。特别是,我们开发了一种有效的方法来学习用户和项目的嵌入表示,然后利用软参数共享策略优化模型参数。在四个数据集上的经验实验表明,CRML模型可以增强幼稚的度量学习模型,并且在协作推荐的准确性方面明显优于最新方法。我们认为优化问题是一个多任务学习问题,其中包括优化度量学习的主要任务和表示学习的两个辅助任务。特别是,我们开发了一种有效的方法来学习用户和项目的嵌入表示,然后利用软参数共享策略优化模型参数。在四个数据集上的经验实验表明,CRML模型可以增强幼稚的度量学习模型,并且在协作推荐的准确性方面明显优于最新方法。然后利用软参数共享策略优化模型参数。在四个数据集上的经验实验表明,CRML模型可以增强幼稚的度量学习模型,并且在协作推荐的准确性方面明显优于最新方法。然后利用软参数共享策略优化模型参数。在四个数据集上的经验实验表明,CRML模型可以增强幼稚的度量学习模型,并且在协作推荐的准确性方面明显优于最新方法。
更新日期:2020-01-31
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