1 code implementation • 15 Jul 2024 • Kaiming Shen, Xichen Ding, Zixiang Zheng, Yuqi Gong, Qianqian Li, Zhongyi Liu, Guannan Zhang
To address these challenges, we propose a unified lifelong multi-modal sequence model called SEMINAR-Search Enhanced Multi-Modal Interest Network and Approximate Retrieval.
no code implementations • 5 Feb 2024 • Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, Wenliang Zhong
We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance.
1 code implementation • 8 Nov 2023 • Zepeng Zhang, Ziping Zhao, Kaiming Shen, Daniel P. Palomar, Wei Yu
By probing the theoretical underpinnings linking the BCA and MM algorithmic frameworks, we reveal the direct correlations between the equivalent transformation techniques, essential to the development of WMMSE and WSR-FP, and the surrogate functions pivotal to WSR-MM.
no code implementations • 16 Sep 2023 • Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, Guannan Zhang
With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks.
no code implementations • 3 Apr 2023 • Mingxiao Li, Rui Jin, Liyao Xiang, Kaiming Shen, Shuguang Cui
The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i. i. d.
1 code implementation • 4 Aug 2018 • Wei Cui, Kaiming Shen, Wei Yu
The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task.