论文标题
大规模实时个性化类似产品建议
Large-scale Real-time Personalized Similar Product Recommendations
论文作者
论文摘要
类似的产品推荐是电子商务中最常见的场景之一。许多建议算法（例如项目到项目协作过滤）正在努力衡量项目相似性。在本文中，我们介绍了实时个性化算法，以建模产品相似性和实时用户兴趣。我们还介绍了其他几种基线算法，包括基于图像相似的方法，项目到项目协作过滤和Item2Vec，并在我们的大型现实世界电子商务数据集中进行比较。在线电子商务网站上还测试了获得良好离线结果的算法。我们的个性化方法在现实世界的电子商务方案中的添加期数字提高了10％。
Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests. We also introduce several other baseline algorithms including an image-similarity-based method, item-to-item collaborative filtering, and item2vec, and compare them on our large-scale real-world e-commerce dataset. The algorithms which achieve good offline results are also tested on the online e-commerce website. Our personalized method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.
