A Graph-based Push Service Platform
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He

TL;DR
This paper presents a graph-based push service platform for mobile app stores that automatically discovers target user groups using a novel graph algorithm, improving recommendation effectiveness in large-scale environments.
Contribution
It introduces a practical graph-based algorithm (A-PARW) for user group discovery, enhancing push recommendation systems over traditional methods like Personalized Pagerank.
Findings
A-PARW significantly improves user group discovery accuracy.
The platform effectively manages web-scale user data.
Enhanced recommendation performance demonstrated.
Abstract
It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line system. There are two different modes of recommendation systems, namely pull-mode and push-mode. The majority of the recommendation systems are pull-mode, which recommend items to users only when and after users enter Application Market. While push-mode works more actively to enhance or re-build connection between Application Market and users. As one of the most successful phone manufactures,both the number of users and apps increase dramatically in Huawei Application Store (also named Hispace Store), which has approximately 0.3 billion registered users and 1.2 million apps until 2016 and whose number of users is growing with high-speed. For the needs of real scenario, we establish a Push Service Platform…
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Taxonomy
TopicsRecommender Systems and Techniques · Caching and Content Delivery · Human Mobility and Location-Based Analysis
