Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Lian, Jianxun"'
Autor:
Wang, Tianfu, Deng, Liwei, Wang, Chao, Lian, Jianxun, Yan, Yue, Yuan, Nicholas Jing, Zhang, Qi, Xiong, Hui
As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evalua
Externí odkaz:
http://arxiv.org/abs/2405.10640
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommenda
Externí odkaz:
http://arxiv.org/abs/2403.06465
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and control
Externí odkaz:
http://arxiv.org/abs/2403.05063
Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intellige
Externí odkaz:
http://arxiv.org/abs/2403.04483
This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturing the nuances necessary for zero-shot performance on item retrieval t
Externí odkaz:
http://arxiv.org/abs/2402.18899
Retrieval models aim at selecting a small set of item candidates which match the preference of a given user. They play a vital role in large-scale recommender systems since subsequent models such as rankers highly depend on the quality of item candid
Externí odkaz:
http://arxiv.org/abs/2401.06633
Autor:
Li, Cheng, Wang, Jindong, Zhang, Yixuan, Zhu, Kaijie, Wang, Xinyi, Hou, Wenxin, Lian, Jianxun, Luo, Fang, Yang, Qiang, Xie, Xing
Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions. This
Externí odkaz:
http://arxiv.org/abs/2312.11111
Recommender systems are widely used in online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often function as a black box, making them less transpar
Externí odkaz:
http://arxiv.org/abs/2311.10947
The significant progress of large language models (LLMs) provides a promising opportunity to build human-like systems for various practical applications. However, when applied to specific task domains, an LLM pre-trained on a general-purpose corpus m
Externí odkaz:
http://arxiv.org/abs/2311.10779
Recommendation systems effectively guide users in locating their desired information within extensive content repositories. Generally, a recommendation model is optimized to enhance accuracy metrics from a user utility standpoint, such as click-throu
Externí odkaz:
http://arxiv.org/abs/2310.13260