Large-scale Real-time Personalized Similar Product Recommendations
This work addresses the need for more effective product recommendations in e-commerce, though it appears incremental as it builds on existing methods like collaborative filtering.
The paper tackled the problem of similar product recommendations in e-commerce by developing a real-time personalized algorithm that models product similarity and user interests, achieving a 10% improvement in add-cart numbers in a real-world scenario.
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.