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A Hybrid Approach for Neural Collaborative Filtering

机译：一种神经协同滤波的混合方法

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Recommender systems enable better personalization for e-commerce and online entertainment services and thus gain significant attention from researchers. Alongside the traditional matrix factorization approach, neural networks have been recently a promising trend for collaborative filtering-based systems thanks to considerable improvements in the quality of the recommendations. This paper introduces a hybrid collaborative filtering framework that applies both approaches in a parallel manner to learn knowledge from implicit feedback data. Embedding vectors representing the information of users and items are first mapped from data. Matrix factorization is generalized by the element-wise product of these embeddings, while the neural network takes as input a 2-D interaction map formed from the stacking of two vectors. The framework fuses the element outputs by concatenation to produce an accurate estimation of the correlation between users and items. The proposed method outperformed several baselines in the experiments on standard datasets, including MovieLens, Yelp, and Pinterest. This advantage suggests more considerations on the integration of deep learning to collaborative filtering for effective recommender systems.
机译：推荐系统为电子商务和在线娱乐服务提供更好的个性化，从而获得了研究人员的重大关注。除了传统的矩阵分解方法方面，由于建议质量的大量改进，神经网络是基于协作过滤系统的有希望的趋势。本文介绍了一种混合协作过滤框架，其以平行方式应用两种方法，以从隐式反馈数据学习知识。代表用户和项目信息的嵌入向量是首先从数据映射的。矩阵分解由这些嵌入的元素 - 方向产品广泛化，而神经网络用作输入由两个向量的堆叠形成的2-D交互图。该框架通过连接来熔化元件输出，以便精确估计用户和项目之间的相关性。该方法在标准数据集的实验中表现出几个基线，包括Movielens，Yelp和Pinterest。这一优势表明，关于对有效推荐系统的协同过滤的深度学习集成的更多考虑因素。

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