Nowadays designing a real recommendation system has been a critical problem
for both academic and industry. However, due to the huge number of users and
items, the diversity and dynamic property of the user interest, how to design a
scalable recommendation system, which is able to efficiently produce effective
and diverse recommendation results on billion-scale scenarios, is still a
challenging and open problem for existing methods. In this paper, given the
user-item interaction graph, we propose RNE, a data-efficient
Recommendation-based Network Embedding method, to give personalized and diverse
items to users. Specifically, we propose a diversity- and dynamics-aware
neighbor sampling method for network embedding. On the one hand, the method is
able to preserve the local structure between the users and items while modeling
the diversity and dynamic property of the user interest to boost the
recommendation quality. On the other hand the sampling method can reduce the
complexity of the whole method theoretically to make it possible for
billion-scale recommendation. We also implement the designed algorithm in a
distributed way to further improves its scalability. Experimentally, we deploy
RNE on a recommendation scenario of Taobao, the largest E-commerce platform in
China, and train it on a billion-scale user-item graph. As is shown on several
online metrics on A/B testing, RNE is able to achieve both high-quality and
diverse results compared with CF-based methods. We also conduct the offline
experiments on Pinterest dataset comparing with several state-of-the-art
recommendation methods and network embedding methods. The results demonstrate
that our method is able to produce a good result while runs much faster than
the baseline methods.

本文提出了一种基于网络嵌入的数据有效性推荐方法 RNE，该方法能够针对亿级场景进行个性化和多样化推荐，同时保持用户和项目之间的本地结构并建模用户兴趣的多样性和动态性以提高推荐质量，而且还能够在分布式方式下实现，显著优化可扩展性。实验证实，在淘宝亿级规模的推荐场景下，RNE 与基于 CF 的方法相比，能够同时实现高质量和多样化结果，在 Pinterest 数据集上也展现出了显著优越性能。