论文标题
隐性反馈深度协作过滤产品建议系统
Implicit Feedback Deep Collaborative Filtering Product Recommendation System
论文作者
论文摘要
在本文中，使用用户项目互动研究了几种具有潜在变量方法的协作过滤（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建议。证明具有神经协作过滤的CF（NCF）可在大型零件供应公司提供的真实世界专有数据集中产生最高标准化的累积累积增益（NDCG）性能。使用贝叶斯优化（BO）在CF框架中使用贝叶斯优化（BO）测试了不同的超参数。审查了诸如点击数据和诸如ClickTrough率（CTR）之类的指标（例如CTR）的外部数据源，以了解对所介绍的工作的潜在扩展。本文所示的工作提供了公司可以使用的技术来提供产品建议，以增强收入，吸引新客户并获得比竞争对手的优势。
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 behaviours. 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 using Bayesian Optimization (BO) 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.
