Zobrazeno 1 - 10
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pro vyhledávání: '"Huang, Junjie"'
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. Multi-s
Externí odkaz:
http://arxiv.org/abs/2410.16080
Autor:
Huang, Junjie, Jiang, Zhihan, Liu, Jinyang, Huo, Yintong, Gu, Jiazhen, Chen, Zhuangbin, Feng, Cong, Dong, Hui, Yang, Zengyin, Lyu, Michael R.
Logs are imperative in the maintenance of online service systems, which often encompass important information for effective failure mitigation. While existing anomaly detection methodologies facilitate the identification of anomalous logs within exte
Externí odkaz:
http://arxiv.org/abs/2409.13561
Autor:
Huang, Junjie, Guo, Daya, Wang, Chenglong, Gu, Jiazhen, Lu, Shuai, Inala, Jeevana Priya, Yan, Cong, Gao, Jianfeng, Duan, Nan, Lyu, Michael R.
Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts' over
Externí odkaz:
http://arxiv.org/abs/2409.13551
Autor:
Huang, Junjie, Zhu, Quanyan
Recent advances in Large Language Models (LLMs) have shown significant potential in enhancing cybersecurity defenses against sophisticated threats. LLM-based penetration testing is an essential step in automating system security evaluations by identi
Externí odkaz:
http://arxiv.org/abs/2407.17788
Autor:
Shi, Suwen, Huang, Ziwei, Gu, Xingxin, Lin, Xu, Zhong, Chaoying, Hang, Junjie, Lin, Jianli, Zhong, Claire Chenwen, Zhang, Lin, Li, Yu, Huang, Junjie
In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational m
Externí odkaz:
http://arxiv.org/abs/2408.00793
Autor:
Huang, Junjie, Chen, Jizheng, Lin, Jianghao, Qin, Jiarui, Feng, Ziming, Zhang, Weinan, Yu, Yong
In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking systems are widely used in the industry, with retrieval and ranking being t
Externí odkaz:
http://arxiv.org/abs/2407.21022
Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extractors and target-specific domains. To tackle this issue, this paper pres
Externí odkaz:
http://arxiv.org/abs/2407.01312
Log parsing serves as an essential prerequisite for various log analysis tasks. Recent advancements in this field have improved parsing accuracy by leveraging the semantics in logs through fine-tuning large language models (LLMs) or learning from in-
Externí odkaz:
http://arxiv.org/abs/2406.07174
Autor:
Huang, Junjie, Cai, Guohao, Zhu, Jieming, Dong, Zhenhua, Tang, Ruiming, Zhang, Weinan, Yu, Yong
Click-through rate (CTR) prediction plays an indispensable role in online platforms. Numerous models have been proposed to capture users' shifting preferences by leveraging user behavior sequences. However, these historical sequences often suffer fro
Externí odkaz:
http://arxiv.org/abs/2404.09578
Autor:
Kuang, Jinxi, Liu, Jinyang, Huang, Junjie, Zhong, Renyi, Gu, Jiazhen, Yu, Lan, Tan, Rui, Yang, Zengyin, Lyu, Michael R.
Due to the scale and complexity of cloud systems, a system failure would trigger an "alert storm", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual
Externí odkaz:
http://arxiv.org/abs/2403.06485