Zobrazeno 1 - 10
of 401
pro vyhledávání: '"Dai, Xinyi"'
Autor:
Li, Qingyao, Xia, Wei, Du, Kounianhua, Dai, Xinyi, Tang, Ruiming, Wang, Yasheng, Yu, Yong, Zhang, Weinan
LLM agents enhanced by tree search algorithms have yielded notable performances in code generation. However, current search algorithms in this domain suffer from low search quality due to several reasons: 1) Ineffective design of the search space for
Externí odkaz:
http://arxiv.org/abs/2409.09584
Autor:
Yang, Yang, Chen, Bo, Zhu, Chenxu, Zhu, Menghui, Dai, Xinyi, Guo, Huifeng, Zhang, Muyu, Dong, Zhenhua, Tang, Ruiming
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing post
Externí odkaz:
http://arxiv.org/abs/2408.07907
Autor:
Zhu, Jiachen, Lin, Jianghao, Dai, Xinyi, Chen, Bo, Shan, Rong, Zhu, Jieming, Tang, Ruiming, Yu, Yong, Zhang, Weinan
We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significant challenge in effectively enhancing recommender systems with logical reasoning abilities and open-wor
Externí odkaz:
http://arxiv.org/abs/2408.03533
Autor:
Chen, Bo, Dai, Xinyi, Guo, Huifeng, Guo, Wei, Liu, Weiwen, Liu, Yong, Qin, Jiarui, Tang, Ruiming, Wang, Yichao, Wu, Chuhan, Wu, Yaxiong, Zhang, Hao
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender sy
Externí odkaz:
http://arxiv.org/abs/2407.10081
Large language models (LLMs) have achieved remarkable progress in the field of natural language processing (NLP), demonstrating remarkable abilities in producing text that resembles human language for various tasks. This opens up new opportunities fo
Externí odkaz:
http://arxiv.org/abs/2406.02368
Autor:
Du, Kounianhua, Chen, Jizheng, Lin, Jianghao, Xi, Yunjia, Wang, Hangyu, Dai, Xinyi, Chen, Bo, Tang, Ruiming, Zhang, Weinan
Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space. Despite the personalization m
Externí odkaz:
http://arxiv.org/abs/2406.00011
Autor:
Zhang, Wenlin, Wu, Chuhan, Li, Xiangyang, Wang, Yuhao, Dong, Kuicai, Wang, Yichao, Dai, Xinyi, Zhao, Xiangyu, Guo, Huifeng, Tang, Ruiming
Recommender systems aim to predict user interest based on historical behavioral data. They are mainly designed in sequential pipelines, requiring lots of data to train different sub-systems, and are hard to scale to new domains. Recently, Large Langu
Externí odkaz:
http://arxiv.org/abs/2404.00702
Autor:
Lin, Jianghao, Chen, Bo, Wang, Hangyu, Xi, Yunjia, Qu, Yanru, Dai, Xinyi, Zhang, Kangning, Tang, Ruiming, Yu, Yong, Zhang, Weinan
Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals
Externí odkaz:
http://arxiv.org/abs/2310.09234
With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vas
Externí odkaz:
http://arxiv.org/abs/2308.01737
Autor:
Lin, Jianghao, Dai, Xinyi, Xi, Yunjia, Liu, Weiwen, Chen, Bo, Zhang, Hao, Liu, Yong, Wu, Chuhan, Li, Xiangyang, Zhu, Chenxu, Guo, Huifeng, Yu, Yong, Tang, Ruiming, Zhang, Weinan
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, conventional recommendation models (CRM) still have some limitations, e.g
Externí odkaz:
http://arxiv.org/abs/2306.05817