SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework

Author:

Dave Daksh1ORCID,Sharma Aditya2,Abdulhamid Shafi’i Muhammad3,Ahmed Adeel4ORCID,Akhunzada Adnan5ORCID,Amin Rashid6

Affiliation:

1. Department of Electrical Electronics, Birla Institute of Technology And Science, Pilani, Rajasthan, India

2. Department of Computer Science and Information Science, Birla Institute of Technology And Science, Pilani, India

3. Department of Information Technology, Science and Technology Division, Community College of Qatar, Doha, Qatar

4. Department of Information Technology, The University of Haripur, Haripur, Pakistan

5. Department of Computer Sciences, Technical University of Denmark, Kongens Lyngby, Denmark

6. Department of Computer Sciences, University of Chakwal, Rawalpindi, Pakistan

Funder

Qatar National Library

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference39 articles.

1. Personalized Mobile App Recommendation by Learning User's Interest from Social Media

2. Simple embedding for link prediction in knowledge graphs;kazemi;Proc Adv Neural Inf Process Syst (NeurIPS),2018

3. Leveraging app usage contexts for app recommendation: a neural approach

4. Embedding entities and relations for learning and inference in knowledge bases;yang;arXiv 1412 6575,2014

5. Multi-view factorization machines for mobile app recommendation based on hierarchical attention

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1. A Review of Knowledge Graph Recommendation Systems Based on VOSviewer;2024 9th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA);2024-04-25

2. Enhancing App Usage Prediction Accuracy With GCN-Transformer Model and Meta-Path Context;IEEE Access;2024

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