Min Zhang
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- ACM Transactions on Information Systems (19)
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- SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (9)
- SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (7)
- SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (7)
- SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (6)
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- CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (5)
- SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (5)
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- CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (3)
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- SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (3)
- SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (3)
- SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (3)
- WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (3)
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- Article
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
- Shenghao Yang
https://ror.org/03cve4549DCST, Tsinghua University, 100084, Beijing, China
Quan Cheng Laboratory, Jinan, China
, - Weizhi Ma
https://ror.org/03cve4549AIR, Tsinghua University, 100084, Beijing, China
, - Peijie Sun
https://ror.org/03cve4549DCST, Tsinghua University, 100084, Beijing, China
, - Min Zhang
https://ror.org/03cve4549DCST, Tsinghua University, 100084, Beijing, China
Quan Cheng Laboratory, Jinan, China
, - Qingyao Ai
https://ror.org/03cve4549DCST, Tsinghua University, 100084, Beijing, China
, - Yiqun Liu
https://ror.org/03cve4549DCST, Tsinghua University, 100084, Beijing, China
, - Mingchen Cai
Meituan Inc, Beijing, China
Database Systems for Advanced Applications•July 2024, pp 381-390• https://doi.org/10.1007/978-981-97-5569-1_25AbstractKnowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, ...
- 0Citation
MetricsTotal Citations0
- Shenghao Yang
- research-articleOpen AccessPublished By ACMPublished By ACM
LeKUBE: A Knowledge Update BEnchmark for Legal Domain
- Changyue Wang
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Weihang Su
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Yiran Hu
School of Law, Tsinghua University, Beijing, China
, - Qingyao Ai
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Yueyue Wu
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Cheng Luo
Quancheng Lab & MegaTech.AI, Jinan, China
, - Yiqun Liu
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Min Zhang
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Shaoping Ma
Department of Computer Science and Technology, Tsinghua University, Beijing, China
SIGIR-AP 2024: Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region•December 2024, pp 175-185• https://doi.org/10.1145/3673791.3698407Recent advances in Large Language Models (LLMs) have significantly shaped the applications of AI in multiple fields, including the studies of legal intelligence. Trained on extensive legal texts, including statutes and legal documents, the legal LLMs can ...
- 0Citation
- 163
- Downloads
MetricsTotal Citations0Total Downloads163Last 12 Months163Last 6 weeks89
- Changyue Wang
- research-articleOpen AccessPublished By ACMPublished By ACM
Feature-Enhanced Neural Collaborative Reasoning for Explainable Recommendation
- Xiaoyu Zhang
Tsinghua University, Beijing, China
, - Shaoyun Shi
Tsinghua University, Beijing, China
, - Yishan Li
Tsinghua University, Beijing, China
, - Weizhi Ma
Tsinghua University, Beijing, China
, - Peijie Sun
Tsinghua University, Beijing, China
, - Min Zhang
Tsinghua University, Beijing, China
ACM Transactions on Information Systems, Volume 43, Issue 1•January 2025, Article No.: 7, pp 1-33 • https://doi.org/10.1145/3690381Providing reasonable explanations for a specific suggestion given by the recommender can help users trust the system more. As logic rule-based inference is concise, transparent, and aligned with human cognition, it can be adopted to improve the ...
- 0Citation
- 798
- Downloads
MetricsTotal Citations0Total Downloads798Last 12 Months798Last 6 weeks231
- Xiaoyu Zhang
- research-articleOpen AccessPublished By ACMPublished By ACM
Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery
- Jiayu Li
DCST, Tsinghua University; Quan Cheng Laboratory, China
, - Aixin Sun
College of Computing and Data Science, Nanyang Technological University, Singapore
, - Weizhi Ma
AIR, Tsinghua University, China
, - Peijie Sun
DCST, Tsinghua University, China
, - Min Zhang
Quan Cheng Laboratory; DCST, Tsinghua University, China
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems•October 2024, pp 643-653• https://doi.org/10.1145/3640457.3688119From e-commerce to music and news, recommender systems are tailored to specific scenarios. While researching generic models applicable to various scenarios is crucial, studying recommendations based on the unique characteristics of a specific and vital ...
- 0Citation
- 549
- Downloads
MetricsTotal Citations0Total Downloads549Last 12 Months549Last 6 weeks147- 1
Supplementary Materialrecsys24-121-appendix.pdf
- Jiayu Li
- research-articleOpen AccessPublished By ACMPublished By ACM
ReChorus2.0: A Modular and Task-Flexible Recommendation Library
- Jiayu Li
DCST, Tsinghua University; Quan Cheng Laboratory, China
, - Hanyu Li
DCST, Tsinghua University; Quan Cheng Laboratory, China
, - Zhiyu He
DCST, Tsinghua University, China
, - Weizhi Ma
AIR, Tsinghua University, China
, - Peijie Sun
DCST, Tsinghua University, China
, - Min Zhang
Quan Cheng Laboratory; DCST, Tsinghua University, China
, - Shaoping Ma
DCST, Tsinghua University, China
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems•October 2024, pp 454-464• https://doi.org/10.1145/3640457.3688076With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings. Therefore, a flexible library is needed to help ...
- 1Citation
- 324
- Downloads
MetricsTotal Citations1Total Downloads324Last 12 Months324Last 6 weeks83
- Jiayu Li
- research-articleOpen AccessPublished By ACMPublished By ACM
Large Language Models as Evaluators for Recommendation Explanations
- Xiaoyu Zhang
Tsinghua University, China
, - Yishan Li
Tsinghua University, China
, - Jiayin Wang
Tsinghua Univeristy, China
, - Bowen Sun
Tsinghua Univeristy, China
, - Weizhi Ma
Tsinghua University, China
, - Peijie Sun
Tsinghua University, China
, - Min Zhang
Tsinghua University, China
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems•October 2024, pp 33-42• https://doi.org/10.1145/3640457.3688075The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating the quality of the explanations remains a challenging and unresolved ...
- 0Citation
- 964
- Downloads
MetricsTotal Citations0Total Downloads964Last 12 Months964Last 6 weeks174
- Xiaoyu Zhang
- research-articlePublished By ACMPublished By ACM
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
- Miaomiao Cai
Hefei University of Technology, Hefei, China
, - Lei Chen
Tsinghua University, Beijing, China
, - Yifan Wang
DCST, Tsinghua University, Beijing, China
, - Haoyue Bai
Hefei University of Technology, Hefei, China
, - Peijie Sun
DCST, Tsinghua University, Beijing, China
, - Le Wu
Hefei University of Technology, Hefei, China
, - Min Zhang
DCST, Tsinghua University & Quan Cheng Laboratory, Beijing, China
, - Meng Wang
Hefei University of Technology, Hefei, China
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2024, pp 187-198• https://doi.org/10.1145/3637528.3671824Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. It not ...
- 1Citation
- 353
- Downloads
MetricsTotal Citations1Total Downloads353Last 12 Months353Last 6 weeks51- 1
Supplementary Material7?3?.mp4
- Miaomiao Cai
- research-articlePublished By ACMPublished By ACM
Double Correction Framework for Denoising Recommendation
- Zhuangzhuang He
Hefei University of Technology, Hefei, China
, - Yifan Wang
Tsinghua University, Beijing, China
, - Yonghui Yang
Hefei University of Technology, Hefei, China
, - Peijie Sun
Tsinghua University, Beijing, China
, - Le Wu
Hefei University of Technology & Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei, China
, - Haoyue Bai
Hefei University of Technology, Hefei, China
, - Jinqi Gong
University of Macau, Macau, China
, - Richang Hong
Hefei University of Technology & Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei, China
, - Min Zhang
Tsinghua University, Beijing, China
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2024, pp 1062-1072• https://doi.org/10.1145/3637528.3671692As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (such as misclicks or non-preferential ...
- 0Citation
- 338
- Downloads
MetricsTotal Citations0Total Downloads338Last 12 Months338Last 6 weeks35- 1
Supplementary Materialok.mov
- Zhuangzhuang He
- research-articleOpen AccessPublished By ACMPublished By ACM
EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation
- Shaorun Zhang
DCST, Tsinghua University, Beijing, China
, - Zhiyu He
DCST, Tsinghua University, Beijing, China
, - Ziyi Ye
DCST, Tsinghua University, Beijing, China
, - Peijie Sun
DCST, Tsinghua University, Beijing, China
, - Qingyao Ai
DCST, Tsinghua University, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Beijing, China
, - Yiqun Liu
DCST, Tsinghua University, Beijing, China
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2024, pp 698-708• https://doi.org/10.1145/3626772.3657890In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems primarily rely on behavioral data, which faces limitations when inferring ...
- 2Citation
- 609
- Downloads
MetricsTotal Citations2Total Downloads609Last 12 Months609Last 6 weeks102
- Shaorun Zhang
- research-articleOpen AccessPublished By ACMPublished By ACM
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
- Yuanqing Yu
DCST, Tsinghua University, Beijing, China
, - Chongming Gao
University of Science and Technology of China, Hefei, China
, - Jiawei Chen
Zhejiang University, Hangzhou, China
, - Heng Tang
Zhejiang University, Hangzhou, China
, - Yuefeng Sun
Zhejiang University, Hangzhou, China
, - Qian Chen
University of Science and Technology of China, Hefei, China
, - Weizhi Ma
AIR, Tsinghua University, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Beijing, China
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2024, pp 977-987• https://doi.org/10.1145/3626772.3657868Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, ...
- 1Citation
- 592
- Downloads
MetricsTotal Citations1Total Downloads592Last 12 Months592Last 6 weeks102
- Yuanqing Yu
- research-articleOpen AccessPublished By ACMPublished By ACM
Sequential Recommendation with Latent Relations based on Large Language Model
- Shenghao Yang
DCST, Tsinghua University, Quan Cheng Laboratory, Beijing, China
, - Weizhi Ma
AIR, Tsinghua University, Beijing, China
, - Peijie Sun
DCST, Tsinghua University, Beijing, China
, - Qingyao Ai
DCST, Tsinghua University, Beijing, China
, - Yiqun Liu
DCST, Tsinghua University, Beijing, China
, - Mingchen Cai
Meituan, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Quan Cheng Laboratory, Beijing, China
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2024, pp 335-344• https://doi.org/10.1145/3626772.3657762Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. ...
- 2Citation
- 1,146
- Downloads
MetricsTotal Citations2Total Downloads1,146Last 12 Months1,146Last 6 weeks242
- Shenghao Yang
- research-articleOpen AccessPublished By ACMPublished By ACM
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
- Hanyu Li
Department of Computer Science and Technology, Quan Cheng Laboratory, Tsinghua University, Beijing, China
, - Weizhi Ma
Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China
, - Peijie Sun
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Jiayu Li
Department of Computer Science and Technology, Tsinghua University, Beijing, China
, - Cunxiang Yin
Tencent, Beijing, China
, - Yancheng He
Tencent, Beijing, China
, - Guoqiang Xu
Tencent, Beijing, China
, - Min Zhang
Department of Computer Science and Technology, Quan Cheng Laboratory, Tsinghua University, Beijing, China
, - Shaoping Ma
Department of Computer Science and Technology, Tsinghua University, Beijing, China
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2024, pp 2081-2090• https://doi.org/10.1145/3626772.3657713As recommender systems become pervasive in various scenarios, cross-domain recommenders (CDR) are proposed to enhance the performance of one target domain with data from other related source domains. However, irrelevant information from the source domain ...
- 1Citation
- 702
- Downloads
MetricsTotal Citations1Total Downloads702Last 12 Months702Last 6 weeks128
- Hanyu Li
- short-paperOpen AccessPublished By ACMPublished By ACM
MACRec: A Multi-Agent Collaboration Framework for Recommendation
- Zhefan Wang
DCST, Tsinghua University, Beijing, China
, - Yuanqing Yu
DCST, Tsinghua University, Beijing, China
, - Wendi Zheng
DCST, Tsinghua University, Beijing, China
, - Weizhi Ma
AIR, Tsinghua University, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Beijing, China
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2024, pp 2760-2764• https://doi.org/10.1145/3626772.3657669LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce ...
- 1Citation
- 1,133
- Downloads
MetricsTotal Citations1Total Downloads1,133Last 12 Months1,133Last 6 weeks291
- Zhefan Wang
- research-articlePublished By ACMPublished By ACM
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
- Peijie Sun
Quan Cheng Laboratory & DCST, Tsinghua University, Jinan, China
, - Yifan Wang
DCST, Tsinghua University, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Beijing, China
, - Chuhan Wu
Noah's Ark Lab, Huawei, Beijing, China
, - Yan Fang
DCST, Tsinghua University, Beijing, China
, - Hong Zhu
Consumer Cloud Service Interactive Media BU, Huawei, Shanzhen, China
, - Yuan Fang
Consumer Cloud Service Interactive Media BU, Huawei, Shenzhen, China
, - Meng Wang
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 10-19• https://doi.org/10.1145/3589335.3648297With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To ...
- 7Citation
- 233
- Downloads
MetricsTotal Citations7Total Downloads233Last 12 Months233Last 6 weeks11
- Peijie Sun
- research-articleOpen AccessPublished By ACMPublished By ACM
Intersectional Two-sided Fairness in Recommendation
- Yifan Wang
Quan Cheng Laboratory & DCST, BNRist, Tsinghua University, Jinan, Shandong, China
, - Peijie Sun
DCST, Tsinghua University, Beijing, China
, - Weizhi Ma
AIR, Tsinghua University, Beijing, China
, - Min Zhang
DCST, Tsinghua University, Beijing, China
, - Yuan Zhang
Kuaishou Technology, Beijing, China
, - Peng Jiang
Kuaishou Technology, Beijing, China
, - Shaoping Ma
DCST, Tsinghua University, Beijing, China
WWW '24: Proceedings of the ACM Web Conference 2024•May 2024, pp 3609-3620• https://doi.org/10.1145/3589334.3645518Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness ...
- 2Citation
- 855
- Downloads
MetricsTotal Citations2Total Downloads855Last 12 Months855Last 6 weeks71- 2
- Yifan Wang
- demonstrationPublished By ACMPublished By ACM
SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological Signals
- Vadim Grigorev
Tsinghua University, China
, - Jiayu Li
Tsinghua University, China
, - Weizhi Ma
Tsinghua University, China
, - Zhiyu He
Tsinghua University, China
, - Min Zhang
Tsinghua University, China
, - Yiqun Liu
Tsinghua University, China
, - Ming Yan
Alibaba Group, China
, - Ji Zhang
Alibaba Group, China
CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval•March 2024, pp 417-421• https://doi.org/10.1145/3627508.3638343With an increasing number of music tracks available online, music recommender systems have become popular and ubiquitous. Previous research indicates that people’s preferences, especially in music, dynamically change with various factors, such as ...
- 0Citation
- 199
- Downloads
MetricsTotal Citations0Total Downloads199Last 12 Months199Last 6 weeks12- 1
Supplementary MaterialAppendix.pdf
- Vadim Grigorev
- research-articlePublished By ACMPublished By ACM
Relevance Feedback with Brain Signals
- Ziyi Ye
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Xiaohui Xie
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Qingyao Ai
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Yiqun Liu
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Zhihong Wang
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Weihang Su
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
, - Min Zhang
Quan Cheng Lab, DCST, Tsinghua University, Zhongguancun Lab, China
ACM Transactions on Information Systems, Volume 42, Issue 4•July 2024, Article No.: 93, pp 1-37 • https://doi.org/10.1145/3637874The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have ...
- 7Citation
- 377
- Downloads
MetricsTotal Citations7Total Downloads377Last 12 Months331Last 6 weeks34
- Ziyi Ye
- research-articleOpen AccessPublished By ACMPublished By ACM
Investigating the Influence of Legal Case Retrieval Systems on Users' Decision Process
- Beining Wang
Department of Computer Science and Technology, Tsinghua University, China
, - Ruizhe Zhang
Department of Computer Science and Technology, Tsinghua University, China
, - Yueyue Wu
Department of Computer Science and Technology, Tsinghua University, China
, - Qingyao Ai
Department of Computer Science and Technology, Tsinghua University, China
, - Min Zhang
Department of Computer Science and Technology, Tsinghua University, China
, - Yiqun Liu
Department of Computer Science and Technology, Tsinghua University, China
SIGIR-AP '23: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region•November 2023, pp 169-175• https://doi.org/10.1145/3624918.3625321Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand. Previous studies on user behavior analysis have shown that information retrieval (IR) systems can significantly influence ...
- 0Citation
- 226
- Downloads
MetricsTotal Citations0Total Downloads226Last 12 Months202Last 6 weeks29
- Beining Wang
- research-articlePublished By ACMPublished By ACM
Incorporating Structural Information into Legal Case Retrieval
- Yixiao Ma
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Yueyue Wu
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Qingyao Ai
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Yiqun Liu
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Yunqiu Shao
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Min Zhang
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
, - Shaoping Ma
Department of Computer Science and Technology, Institute for Internet Judiciary, Tsinghua University. Quan Cheng Laboratory, China
ACM Transactions on Information Systems, Volume 42, Issue 2•March 2024, Article No.: 40, pp 1-28 • https://doi.org/10.1145/3609796Legal case retrieval has received increasing attention in recent years. However, compared to ad hoc retrieval tasks, legal case retrieval has its unique challenges. First, case documents are rather lengthy and contain complex legal structures. Therefore, ...
- 3Citation
- 899
- Downloads
MetricsTotal Citations3Total Downloads899Last 12 Months517Last 6 weeks32
- Yixiao Ma
- research-articleOpen AccessPublished By ACMPublished By ACM
Understanding User Immersion in Online Short Video Interaction
- Zhiyu He
Tsinghua University, Beijing, China
, - Shaorun Zhang
Tsinghua University, Beijing, China
, - Peijie Sun
Tsinghua University, Beijing, China
, - Jiayu Li
Tsinghua University, Beijing, China
, - Xiaohui Xie
Tsinghua University, Beijing, China
, - Min Zhang
Tsinghua University, Beijing, China
, - Yiqun Liu
Tsinghua University, Beijing, China
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 731-740• https://doi.org/10.1145/3583780.3615099Short video~(SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in ...
- 3Citation
- 894
- Downloads
MetricsTotal Citations3Total Downloads894Last 12 Months672Last 6 weeks90
- Zhiyu He
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
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These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
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- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner