Large-scale Real-time Personalized Similar Product Recommendations
Information Retrieval
2020-04-14 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2004.05716,
title = {Large-scale Real-time Personalized Similar Product Recommendations},
author = {Zhi Liu and Yan Huang and Jing Gao and Li Chen and Dong Li},
journal= {arXiv preprint arXiv:2004.05716},
year = {2020}
}