Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Ni, Yongxin"'
Autor:
Fu, Junchen, Ge, Xuri, Xin, Xin, Karatzoglou, Alexandros, Arapakis, Ioannis, Zheng, Kaiwen, Ni, Yongxin, Jose, Joemon M.
Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt these models, studies often prioritize parameter e
Externí odkaz:
http://arxiv.org/abs/2411.02992
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and
Externí odkaz:
http://arxiv.org/abs/2403.17372
ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developin
Externí odkaz:
http://arxiv.org/abs/2312.09602
Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhibit notable improvement in reco
Externí odkaz:
http://arxiv.org/abs/2310.17373
Autor:
Ni, Yongxin, Cheng, Yu, Liu, Xiangyan, Fu, Junchen, Li, Youhua, He, Xiangnan, Zhang, Yongfeng, Yuan, Fajie
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries. However, the lack of large-scale public micro-
Externí odkaz:
http://arxiv.org/abs/2309.15379
Autor:
Li, Jiakang, Lai, Songning, Shuai, Zhihao, Tan, Yuan, Jia, Yifan, Yu, Mianyang, Song, Zichen, Peng, Xiaokang, Xu, Ziyang, Ni, Yongxin, Qiu, Haifeng, Yang, Jiayu, Liu, Yutong, Lu, Yonggang
The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biolog
Externí odkaz:
http://arxiv.org/abs/2309.11798
Autor:
Zhang, Jiaqi, Cheng, Yu, Ni, Yongxin, Pan, Yunzhu, Yuan, Zheng, Fu, Junchen, Li, Youhua, Wang, Jie, Yuan, Fajie
Large foundational models, through upstream pre-training and downstream fine-tuning, have achieved immense success in the broad AI community due to improved model performance and significant reductions in repetitive engineering. By contrast, the tran
Externí odkaz:
http://arxiv.org/abs/2309.07705
Recommender systems (RS) have achieved significant success by leveraging explicit identification (ID) features. However, the full potential of content features, especially the pure image pixel features, remains relatively unexplored. The limited avai
Externí odkaz:
http://arxiv.org/abs/2309.06789
Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendation systems, research on multi-m
Externí odkaz:
http://arxiv.org/abs/2308.04067
Autor:
Yuan, Zheng, Yuan, Fajie, Song, Yu, Li, Youhua, Fu, Junchen, Yang, Fei, Pan, Yunzhu, Ni, Yongxin
Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders,
Externí odkaz:
http://arxiv.org/abs/2303.13835