Zobrazeno 1 - 10
of 1 190
pro vyhledávání: '"Xu Xiaoxiao"'
User behavior modeling -- which aims to extract user interests from behavioral data -- has shown great power in Click-through rate (CTR) prediction, a key component in recommendation systems. Recently, attention-based algorithms have become a promisi
Externí odkaz:
http://arxiv.org/abs/2410.15098
Autor:
Zhu, Xiangru, Sun, Penglei, Song, Yaoxian, Xiao, Yanghua, Li, Zhixu, Wang, Chengyu, Huang, Jun, Yang, Bei, Xu, Xiaoxiao
Accurate interpretation and visualization of human instructions are crucial for text-to-image (T2I) synthesis. However, current models struggle to capture semantic variations from word order changes, and existing evaluations, relying on indirect metr
Externí odkaz:
http://arxiv.org/abs/2410.10291
A conjecture due to Y. Han asks whether that Hochschild homology groups of a finite dimensional algebra vanish for sufficiently large degrees would imply that the algebra is of finite global dimension. We investigate this conjecture from the viewpoin
Externí odkaz:
http://arxiv.org/abs/2409.00945
An extension $B\subset A$ of finite dimensional algebras is bounded if the $B$-$B$-bimodule $A/B$ is $B$-tensor nilpotent, its projective dimension is finite and $\mathrm{Tor}_i^B(A/B, (A/B)^{\otimes_B j})=0$ for all $i, j\geq 1$. We show that for a
Externí odkaz:
http://arxiv.org/abs/2407.21480
Autor:
Liu, Nian, Fan, Shen, Bai, Ting, Wang, Peng, Sun, Mingwei, Mo, Yanhu, Xu, Xiaoxiao, Liu, Hong, Shi, Chuan
Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to their inferior quality and insufficient quantity, especially for inac
Externí odkaz:
http://arxiv.org/abs/2405.05288
Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement l
Externí odkaz:
http://arxiv.org/abs/2405.01847
Autor:
Zhang, Mengmei, Sun, Mingwei, Wang, Peng, Fan, Shen, Mo, Yanhu, Xu, Xiaoxiao, Liu, Hong, Yang, Cheng, Shi, Chuan
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks. While the idea is less explored in the
Externí odkaz:
http://arxiv.org/abs/2402.07197
We introduce \textit{GraphGPT}, a novel model for Graph learning by self-supervised Generative Pre-training Transformers. Our model transforms each graph or sampled subgraph into a sequence of tokens representing the node, edge and attributes reversi
Externí odkaz:
http://arxiv.org/abs/2401.00529
Autor:
Xu, Xiaoxiao1,2 (AUTHOR), Fu, Yanyan1 (AUTHOR), Luo, Delun2 (AUTHOR), Zhang, Lina3 (AUTHOR), Huang, Xi1 (AUTHOR), Chen, Yingying1 (AUTHOR), Lei, Chunyan1 (AUTHOR), Liu, Jinnan2 (AUTHOR), Li, Shiqi2 (AUTHOR), Yu, Zhouyuan2 (AUTHOR), Lin, Yunfeng4 (AUTHOR), Zhang, Meixia1 (AUTHOR) zhangmeixia@scu.edu.cn
Publikováno v:
Cell Proliferation. Nov2024, Vol. 57 Issue 11, p1-15. 15p.
Autor:
Dang, Yizhou, Yang, Enneng, Guo, Guibing, Jiang, Linying, Wang, Xingwei, Xu, Xiaoxiao, Sun, Qinghui, Liu, Hong
Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time i
Externí odkaz:
http://arxiv.org/abs/2212.08262