A Knowledge Graph based Approach for Mobile Application Recommendation

TL;DR

This paper introduces a novel end-to-end knowledge graph convolutional embedding model for mobile app recommendation, effectively utilizing rich user and app information to improve recommendation accuracy.

Contribution

The paper presents a new KG-based approach combining KG construction, embedding, and relation-weighted propagation for enhanced app recommendation.

Findings

Outperforms state-of-the-art recommendation methods on real-world data

Effectively models high-order semantic relations in knowledge graphs

Addresses data sparsity issues in app recommendation systems

Abstract

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.