A recommender system to address the Cold Start problem for App usage prediction-Reference-Cited by-同舟云学术

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A recommender system to address the Cold Start problem for App usage prediction

Published:2018-08-10 Issue:9 Volume:10 Page:2257-2268
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Han Di,Li Jianqing,Yang Lei,Zeng Zihua

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-018-0864-z.pdf

Reference27 articles.

1. Xiang Z, Deng S, Liu S, Cao B, Yin J (2016) Camer: a context-aware mobile service recommendation system. In: 2016 IEEE international conference on web services (ICWS), pp 292–299

2. Baeza-Yates R, Jiang D, Silvestri F, Harrison B (2015) Predicting the next app that you are going to use. In: Proceedings of the eighth ACM international conference on web search and data mining, pp 285–294

3. Moshfeghi Y, Piwowarski B, Jose JM (2011) Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 625–634

4. Liu NN, Meng X, Liu C, Yang Q (2011) Wisdom of the better few: cold start recommendation via representative based rating elicitation. In: Proceedings of the fifth ACM conference on recommender systems, pp 37–44

5. Zhou K, Yang SH, Zha H (2011) Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 315–324

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mitigation of the cold start problem in movie recommender systems using fuzzification of users’ social network information;2024 10th International Conference on Artificial Intelligence and Robotics (QICAR);2024-02-29

2. Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review;Journal of Intelligent Information Systems;2022-04-23

3. Smartphone App Usage Analysis: Datasets, Methods, and Applications;IEEE Communications Surveys & Tutorials;2022

4. Prediction of Application Usage on Smartphones via Deep Learning;IEEE Access;2022

5. AND: Effective Coupling of Accuracy, Novelty and Diversity in the Recommender System;2021 17th International Conference on Mobility, Sensing and Networking (MSN);2021-12

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