Results 11 to 20 of about 2,553,529 (294)

Transferable Fairness for Cold-Start Recommendation [PDF]

CoRR, 2023
With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem.
Wang, Dingxian, Zhang, Yongfeng, Chen, Hanxiong, Li, Yunqi   +3 more
core   +3 more sources

Deep Pairwise Hashing for Cold-Start Recommendation [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2020
Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy instead of efficiency.
Tsang, IW, Lian, D, Li, J, Zhang, Y, Yang, G, Yin, H, Guowu Yang, Defu Lian, Jingjing Li, Yan Zhang, Hongzhi Yin, Ivor Tsang   +11 more
core   +6 more sources

Blockchain-based service publishing scheme with recommendation for cold start users [PDF]

Tongxin xuebao, 2021
In order to solve the problem that the existing publishing systems were vulnerable to tampering, and it was difficult to recommend service information to “cold start” users, a blockchain-based service publishing scheme that supported cold-start user ...
Xuewen DONG, Haozhe LIU, Hui QIAO, Jiawei ZHENG   +3 more
doaj   +4 more sources

Making a Cold Start in Legal Recommendation: an Experiment [PDF]

, 2016
Since the OpenLaws portal is envisioned as an open environment for collaboration between legal professionals, recommendation will eventually become a collaborative filtering problem.
Winkels, R.; id_orcid, Boer, A., Winkels, R.   +2 more
core   +5 more sources

An Industrial Framework for Cold-Start Recommendation in Few-Shot and Zero-Shot Scenarios

Information
With the rise of online advertising, e-commerce industries, and new media platforms, recommendation systems have become an essential product form that connects users with a vast number of candidates.
Xulei Cao, Wenyu Zhang, Feiyang Jiang, Xinming Zhang   +3 more
doaj   +2 more sources

Contrastive Learning for Cold-Start Recommendation [PDF]

Proceedings of the 29th ACM International Conference on Multimedia, 2021
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information into the CF scheme ...
Yinwei Wei, Xiang Wang 0010, Qi Li, Liqiang Nie, Yan Li 0068, Xuanping Li, Tat-Seng Chua   +6 more
openaire   +3 more sources

Sparsity Regularization For Cold-Start Recommendation

CoRR, 2022
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making ...
Aksheshkumar Ajaykumar Shah, Hemanth Venkateswara   +1 more
openaire   +2 more sources

Factorizing LambdaMART for cold start recommendations [PDF]

Machine Learning, 2016
Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric.
Phong Nguyen 0002, Jun Wang 0017, Alexandros Kalousis   +2 more
openaire   +3 more sources

MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation

Applied Sciences, 2023
Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches ...
Yicheng Di, Yuan Liu
doaj   +1 more source

Task-adaptive neural process for user cold-start recommendation [PDF]

, 2021
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited.
Bin Wang, Zhou, C, Xixun Lin, Cao, Yanan, Jia Wu, Wu, J, Yanan Cao, Cao, Y, Wang, B, Lin, Xixun, Wang, Bin, Pan, S, Wu, Jia, Shirui Pan, Zhou, Chuan, Pan, Shirui, Chuan Zhou, Lin, X   +17 more
core   +1 more source