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

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 comparing 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.

[1]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[2]  Bo Zhang,et al.  Discriminative Deep Random Walk for Network Classification , 2016, ACL.

[3]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[4]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[5]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[6]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[7]  Juan-Zi Li,et al.  Text-Enhanced Representation Learning for Knowledge Graph , 2016, IJCAI.

[8]  Deli Zhao,et al.  Network Representation Learning with Rich Text Information , 2015, IJCAI.

[9]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[10]  Zhiyuan Liu,et al.  TransNet: Translation-Based Network Representation Learning for Social Relation Extraction , 2017, IJCAI.

[11]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[12]  Alexander J. Smola,et al.  Distributed large-scale natural graph factorization , 2013, WWW.

[13]  Zhiyuan Liu,et al.  CANE: Context-Aware Network Embedding for Relation Modeling , 2017, ACL.

[14]  Zhiyuan Liu,et al.  Representation Learning of Knowledge Graphs with Hierarchical Types , 2016, IJCAI.

[15]  Charu C. Aggarwal,et al.  Heterogeneous Network Embedding via Deep Architectures , 2015, KDD.

[16]  Wei Lu,et al.  Deep Neural Networks for Learning Graph Representations , 2016, AAAI.

[17]  Yizhou Sun,et al.  Personalized entity recommendation: a heterogeneous information network approach , 2014, WSDM.

[18]  Rui Zhang,et al.  Incorporating Knowledge Graph Embeddings into Topic Modeling , 2017, AAAI.

[19]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[20]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[21]  Dik Lun Lee,et al.  Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks , 2017, KDD.

[22]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[23]  Chang Zhou,et al.  Scalable Graph Embedding for Asymmetric Proximity , 2017, AAAI.

[24]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[25]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[26]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[27]  Nitesh V. Chawla,et al.  metapath2vec: Scalable Representation Learning for Heterogeneous Networks , 2017, KDD.

[28]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[29]  Zhiyuan Liu,et al.  Max-Margin DeepWalk: Discriminative Learning of Network Representation , 2016, IJCAI.

[30]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[31]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.