In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviors. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering (NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.

中文翻译：

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隐式反馈深度协同过滤产品推荐系统

在本文中，使用用户-项目交互研究了几种具有潜在变量方法的协同过滤 (CF) 方法，以捕获稀疏客户购买行为的重要隐藏变化。潜在因素用于概括客户的购买模式并提供产品推荐。具有神经协同过滤 (NCF) 的 CF 被证明可以在大型零件供应公司提供的真实世界专有数据集上产生最高的归一化折扣累积增益 (NDCG) 性能。测试了不同超参数在 CF 框架中的适用性。外部数据源（如点击数据）和指标（如点击率 (CTR)）都经过审查，以便对所呈现的工作进行潜在的扩展。