Zobrazeno 1 - 10
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pro vyhledávání: '"Fan, Jianping"'
Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based recommend
Externí odkaz:
http://arxiv.org/abs/2410.20642
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:
Ye, Junjie, Yang, Yuming, Zhang, Qi, Gui, Tao, Huang, Xuanjing, Wang, Peng, Shi, Zhongchao, Fan, Jianping
Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can then be fine-tuned for the question-answering (QA) task. However, effective strategies for fine-tuning LLMs for the QA task remain large
Externí odkaz:
http://arxiv.org/abs/2409.15825
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
The attainment of autonomous operations in mobile computing devices has consistently been a goal of human pursuit. With the development of Large Language Models (LLMs) and Visual Language Models (VLMs), this aspiration is progressively turning into r
Externí odkaz:
http://arxiv.org/abs/2407.03913
Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surroun
Externí odkaz:
http://arxiv.org/abs/2406.17475
Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transfo
Externí odkaz:
http://arxiv.org/abs/2406.16494
Autor:
Feng, Xiang, He, Yongbo, Wang, Yubo, Yang, Yan, Li, Wen, Chen, Yifei, Kuang, Zhenzhong, ding, Jiajun, Fan, Jianping, Jun, Yu
Recently, 3D Gaussian Splatting (3DGS) has gained popularity as a novel explicit 3D representation. This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering. However, primitives optimized at low reso
Externí odkaz:
http://arxiv.org/abs/2404.10318
Autor:
Hou, Feng, Yuan, Jin, Yang, Ying, Liu, Yang, Zhang, Yang, Zhong, Cheng, Shi, Zhongchao, Fan, Jianping, Rui, Yong, He, Zhiqiang
Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain t
Externí odkaz:
http://arxiv.org/abs/2403.02714
Autor:
Du, Zhenzhen, Yang, Yujie, Zheng, Jing, Li, Qi, Lin, Denan, Li, Ye, Fan, Jianping, Cheng, Wen, Chen, Xie-Hui, Cai, Yunpeng
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 7, p e17257 (2020)
BackgroundPredictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the dat
Externí odkaz:
https://doaj.org/article/e66f0240f1a4495ab31e31cbd8f96a6b
