Zobrazeno 1 - 10
of 463
pro vyhledávání: '"Lin, Xinyu"'
Autor:
Wang, Wenjie, Bao, Honghui, Lin, Xinyu, Zhang, Jizhi, Li, Yongqi, Feng, Fuli, Ng, See-Kiong, Chua, Tat-Seng
Harnessing Large Language Models (LLMs) for generative recommendation has garnered significant attention due to LLMs' powerful capacities such as rich world knowledge and reasoning. However, a critical challenge lies in transforming recommendation da
Externí odkaz:
http://arxiv.org/abs/2405.07314
Autor:
Li, Yongqi, Lin, Xinyu, Wang, Wenjie, Feng, Fuli, Pang, Liang, Li, Wenjie, Nie, Liqiang, He, Xiangnan, Chua, Tat-Seng
With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with
Externí odkaz:
http://arxiv.org/abs/2404.16924
Autor:
Wang, Wenjie, Zhang, Yang, Lin, Xinyu, Feng, Fuli, Liu, Weiwen, Liu, Yong, Zhao, Xiangyu, Zhao, Wayne Xin, Song, Yang, He, Xiangnan
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innov
Externí odkaz:
http://arxiv.org/abs/2403.04399
Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits the
Externí odkaz:
http://arxiv.org/abs/2401.17197
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumb
Externí odkaz:
http://arxiv.org/abs/2312.09901
Large Language Models (LLMs) have garnered considerable attention in recommender systems. To achieve LLM-based recommendation, item indexing and generation grounding are two essential steps, bridging between recommendation items and natural language.
Externí odkaz:
http://arxiv.org/abs/2310.06491
Publikováno v:
IEEE Transactions on Instrumentation and Measurement 2023
Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks
Externí odkaz:
http://arxiv.org/abs/2305.07943
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. Ho
Externí odkaz:
http://arxiv.org/abs/2305.05883
An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks.
Externí odkaz:
http://arxiv.org/abs/2305.00264
Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as
Externí odkaz:
http://arxiv.org/abs/2304.04971