Results 271 to 280 of about 2,553,529 (294)

Meta-Learning for User Cold-Start Recommendation

2019 International Joint Conference on Neural Networks (IJCNN), 2019
Recent studies in recommender systems emphasize the importance of dealing with the cold-start problem i.e. the modeling of new users or items in the recommendation system. Meta-learning approaches have gained popularity recently in the Machine Learning (ML) community for learning representations useful for a wide-range of tasks.
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Improving the Personalized Recommendation in the Cold-start Scenarios

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019
Recommender systems generate items that should be interesting for the customers. However, recommenders usually fail in the cold-start scenario - when a new item or a new customer appears. In our work, we study the cold-start problem for a new customer.
Peter Gaspar, Michal Kompan, Matej Koncal, Mária Bieliková   +3 more
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Analysis of cold-start recommendations in IPTV systems

Proceedings of the third ACM conference on Recommender systems, 2009
In this paper we evaluate the performance of different collaborative algorithms in cold-start situations, where the initial lack of ratings may affect the quality of the algorithms. The evaluation has been performed on the pay-per-view datasets collected by two IP-television providers over a period of several months.
CREMONESI, PAOLO, TURRIN, ROBERTO
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MetricRec: Metric Learning for Cold-Start Recommendations

2016
Making recommendations for new users is a challenging task of cold-start recommendations due to the absence of historical ratings. When the attributes of users are available, such as age, occupation and gender, then new users’ preference can be inferred.
Furong Peng, Xuan Lu 0001, Jianfeng Lu 0003, Richard Yi Da Xu, Cheng Luo, Chao Ma 0002, Jingyu Yang 0001   +6 more
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TrustRank: a Cold-Start tolerant recommender system

Enterprise Information Systems, 2013
The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention.
Haitao Zou, Zhiguo Gong, Nan Zhang 0004, Wei Zhao 0001, Jingzhi Guo   +4 more
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Addressing Cold Start for Next-song Recommendation

Proceedings of the 10th ACM Conference on Recommender Systems, 2016
The cold start problem arises in various recommendation applications. In this paper, we propose a tensor factorization-based algorithm that exploits content features extracted from music audio to deal with the cold start problem for the emerging application next-song recommendation.
Szu-Yu Chou, Yi-Hsuan Yang, Jyh-Shing Roger Jang, Yu-Ching Lin   +3 more
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GoRec: A Generative Cold-start Recommendation Framework

Proceedings of the 31st ACM International Conference on Multimedia, 2023
Haoyue Bai 0002, Min Hou 0004, Le Wu, Yonghui Yang 0001, Kun Zhang 0015, Richang Hong, Meng Wang 0001   +6 more
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User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network

ACM Transactions on Information Systems, 2023
Changsheng Xu, Shengsheng Qian, Desheng Cai   +2 more
exaly  

A Systematic Review of Rehabilitation and Exercise Recommendations in Oncology Guidelines

Ca-A Cancer Journal for Clinicians, 2021
Julie K. Silver, Nicole L Stout, Kathleen Doyle Lyons   +2 more
exaly  

Federated Against the Cold: A Trust-Based Federated Learning Approach to Counter the Cold Start Problem in Recommendation Systems

Information Sciences, 2022
Jamal Bentahar, Omar Abdel Wahab, Gaith Rjoub   +2 more
exaly