Zobrazeno 1 - 10
of 455
pro vyhledávání: '"Zhuang, Fuzhen"'
As trustworthy AI continues to advance, the fairness issue in recommendations has received increasing attention. A recommender system is considered unfair when it produces unequal outcomes for different user groups based on user-sensitive attributes
Externí odkaz:
http://arxiv.org/abs/2410.17555
Autor:
Jiang, Ting, Song, Minghui, Zhang, Zihan, Huang, Haizhen, Deng, Weiwei, Sun, Feng, Zhang, Qi, Wang, Deqing, Zhuang, Fuzhen
Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding. However, the representation of multimodal information using MLLMs remains largely unexplored. In this work, we introduce a new fr
Externí odkaz:
http://arxiv.org/abs/2407.12580
Autor:
Wei, Shaopeng, Egressy, Beni, Chen, Xingyan, Zhao, Yu, Zhuang, Fuzhen, Wattenhofer, Roger, Kou, Gang
Enterprise credit assessment is critical for evaluating financial risk, and Graph Neural Networks (GNNs), with their advanced capability to model inter-entity relationships, are a natural tool to get a deeper understanding of these financial networks
Externí odkaz:
http://arxiv.org/abs/2407.11615
Autor:
Zhang, Yuting, Wu, Yiqing, Han, Ruidong, Sun, Ying, Zhu, Yongchun, Li, Xiang, Lin, Wei, Zhuang, Fuzhen, An, Zhulin, Xu, Yongjun
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging i
Externí odkaz:
http://arxiv.org/abs/2407.00912
Autor:
Chen, Xi, Qin, Chuan, Fang, Chuyu, Wang, Chao, Zhu, Chen, Zhuang, Fuzhen, Zhu, Hengshu, Xiong, Hui
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitive
Externí odkaz:
http://arxiv.org/abs/2406.11920
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhang, Xu, Zhuang, Fuzhen, Lin, Leyu, Kang, Zhanhui, Xu, Yongjun
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike,
Externí odkaz:
http://arxiv.org/abs/2405.15280
Autor:
Jiang, Ting, Huang, Shaohan, Luo, Shengyue, Zhang, Zihan, Huang, Haizhen, Wei, Furu, Deng, Weiwei, Sun, Feng, Zhang, Qi, Wang, Deqing, Zhuang, Fuzhen
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA. Our findings suggest that the low-rank updating mechanism may limit
Externí odkaz:
http://arxiv.org/abs/2405.12130
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhuang, Fuzhen, Zhang, Xu, Lin, Leyu, Kang, Zhanhui, Xu, Yongjun
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of p
Externí odkaz:
http://arxiv.org/abs/2405.03562
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi
Externí odkaz:
http://arxiv.org/abs/2404.08361
Autor:
Jiang, Feihu, Qin, Chuan, Yao, Kaichun, Fang, Chuyu, Zhuang, Fuzhen, Zhu, Hengshu, Xiong, Hui
Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge through vectorization, a variety of knowledge retrieval methods have
Externí odkaz:
http://arxiv.org/abs/2404.08695