Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shen Chenglei"'
Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity). Ideally, the model should be re-trained after resetting a new objective f
Externí odkaz:
http://arxiv.org/abs/2410.10639
Autor:
Qin, Weicong, Xu, Yi, Yu, Weijie, Shen, Chenglei, Zhang, Xiao, He, Ming, Fan, Jianping, Xu, Jun
Sequence recommendation (SeqRec) aims to predict the next item a user will interact with by understanding user intentions and leveraging collaborative filtering information. Large language models (LLMs) have shown great promise in recommendation task
Externí odkaz:
http://arxiv.org/abs/2409.06377
Controllable learning (CL) emerges as a critical component in trustworthy machine learning, ensuring that learners meet predefined targets and can adaptively adjust without retraining according to the changes in those targets. We provide a formal def
Externí odkaz:
http://arxiv.org/abs/2407.06083
Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making abilities of
Externí odkaz:
http://arxiv.org/abs/2402.18807
In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existing bandit-based streaming recommendation models only consider time as a
Externí odkaz:
http://arxiv.org/abs/2308.08497
Publikováno v:
Geoinformatics
Land covers in urban areas trend to change more drastically over a short period of time than elsewhere because of incessant urbanization. These changes are ideally monitored and detected from remotely sensed images as they are relatively up-to-date a
Publikováno v:
2010 18th International Conference on Geoinformatics; 2010, p1-5, 5p