A Graph-Based Push Service Platform

By Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. 2016

6 citations recorded by the source corpus.

Learning users’ preference and making recommendations is critical in information-exploded environment. There are two typical modes for recommendation, known as pull and push, which respectively account for recommendation inside and outside the item market. While previously most recommender systems adopt only pull-mode, push-mode becomes popular in today’s mobile environment. This paper presents a push recommendation platform successfully deployed for Huawei App Store, which has reached 0.3 billion registered users and 1.2 million Apps by 2016. Among the various modules in developing this push platform, we recognized the task of target user group discovery to be most essential in terms of CTR. We explored various algorithmic choices for mining target user group, and highlighted one based on recent advance in graph mining, the Partially Absorbing Random Walk [13], which leads to substantial improvement for our push recommendation, compared to the state-of-the-art including the popular PageRank. We also covered our practice in deploying our push platform in both single server and distributed cluster.

Subfields: Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Neil Zhenqiang Gong
2. Minghong Fang
3. Guolei Yang
4. Jia Liu
5. Binghui Wang
6. Jinyuan Jia
7. Le Zhang

In Information Systems

1. Neil Zhenqiang Gong
2. Minghong Fang
3. Guolei Yang
4. Jia Liu
5. Binghui Wang
6. Jinyuan Jia
7. Junlin Yang
8. Le Zhang

Citations in the corpus

• Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “…recommender systems are deployed by Netflix to recommend movies, association-rule-based recommender systems are deployed by YouTube to recommend videos [6], and graph-based recommender systems are deployed by eBay [25, 26] and Huawei App Store (a big app store in China) [12, 13].”
• Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, background): “As mentioned in Section 4, the computation can be further speeded up using vertex-centric graph engines (Guo et al. 2017), so the scalability of our method is not an issue.”