Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

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 corresponding side information. Experimental results from offline experiments show that methods incorporating side information are superior to those that do not. Further, we describe the platform upon which the embedding methods are deployed and the workflow to process the billion-scale data in Taobao. Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.