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

Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee

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. There are three major challenges facing RS in Taobao: scalability, sparsity, and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on a well-known graph embedding framework. We first construct an item graph from users’ behavior history, and learn the embeddings of all
items in the graph. 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 graph
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 A/B test, we show that the online Click-Through-Rates (CTRs) are improved compared to
the previous collaborative filtering based methods widely used in Taobao, further demonstrating the effectiveness and feasibility of
our proposed methods in Taobao’s live production environment.