  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.
