Zobrazeno 1 - 10
of 859
pro vyhledávání: '"Zhang, Weinan"'
Autor:
Zhu, Chenxu, Quan, Shigang, Chen, Bo, Lin, Jianghao, Cai, Xiaoling, Zhu, Hong, Li, Xiangyang, Xi, Yunjia, Zhang, Weinan, Tang, Ruiming
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While leveraging semantic information from LLMs has shown some improvements in
Externí odkaz:
http://arxiv.org/abs/2411.14713
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an a
Externí odkaz:
http://arxiv.org/abs/2411.14033
Publikováno v:
ACM Transactions on Information Systems 40(1): 9:1-9:44 (2022)
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we st
Externí odkaz:
http://arxiv.org/abs/2411.09166
Argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. Although current AEG methods can generate individual opinions, they often overlook the high-level connections between these opinions. Th
Externí odkaz:
http://arxiv.org/abs/2410.22642
Autor:
Weng, Muyan, Xi, Yunjia, Liu, Weiwen, Chen, Bo, Lin, Jianghao, Tang, Ruiming, Zhang, Weinan, Yu, Yong
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level positive feedback as history (e.g., only clicked items) and ignore that use
Externí odkaz:
http://arxiv.org/abs/2410.20778
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance lack well guided, such as the s
Externí odkaz:
http://arxiv.org/abs/2410.18430
Autor:
Zhang, Kangning, Jin, Jiarui, Qin, Yingjie, Su, Ruilong, Lin, Jianghao, Yu, Yong, Zhang, Weinan
Current multimodal recommendation models have extensively explored the effective utilization of multimodal information; however, their reliance on ID embeddings remains a performance bottleneck. Even with the assistance of multimodal information, opt
Externí odkaz:
http://arxiv.org/abs/2410.19276
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. Multi-s
Externí odkaz:
http://arxiv.org/abs/2410.16080
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, inc
Externí odkaz:
http://arxiv.org/abs/2410.12865
What will information entry look like in the next generation of digital products? Since the 1970s, user access to relevant information has relied on domain-specific architectures of information retrieval (IR). Over the past two decades, the advent of
Externí odkaz:
http://arxiv.org/abs/2410.09713