Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik, Lun Lee

TL;DR
This paper introduces a graph embedding-based recommendation system for Alibaba's Taobao, effectively addressing scalability, sparsity, and cold start issues with billion-scale data, resulting in improved online CTRs.
Contribution
The paper presents a scalable graph embedding framework incorporating side information to enhance recommendation quality at billion-scale data in e-commerce.
Findings
Side information improves embedding quality and recommendation accuracy.
The proposed methods outperform previous approaches in offline experiments.
Online A/B tests show increased click-through rates in production.
Abstract
Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. The billion-scale data in Taobao creates three major challenges to Taobao's RS: scalability, sparsity and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on the graph embedding framework. We first construct an item graph from users' behavior history. Each item is then represented as a vector using graph embedding. The item embeddings are employed to compute pairwise similarities between all items, which are then used in the recommendation process. To alleviate the sparsity and cold start problems, side information is incorporated into the embedding framework. We propose two aggregation methods to integrate the embeddings of items and the…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Caching and Content Delivery
