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arXiv · 2004.05716

Large-scale Real-time Personalized Similar Product Recommendations

Zhi Liu·Yan Huang·Jing Gao·Li Chen·Dong Li
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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.

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Zhi Liu, Yan Huang, Jing Gao, Li Chen, Dong Li. 2020-04-12. Large-scale Real-time Personalized Similar Product Recommendations. https://arxiv.org/abs/2004.05716

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