Jin Chen
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- CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (2)
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- CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (1)
- Database Systems for Advanced Applications. DASFAA 2023 International Workshops (1)
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- research-article
Knowledge distillation for high dimensional search index
- Zepu Lu
School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China
, - Jin Chen
University of Electronic Science and Technology of China
, - Defu Lian
School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China
, - Zaixi Zhang
School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China
, - Yong Ge
University of Arizona
, - Enhong Chen
School of Computer Science and Technology, University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 1452, pp 33403-33419Lightweight compressed indexes are prevalent in Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval efficiency in large-scale datasets. However, results given by compressed indexes ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3666122.3667574_supp.pdf
- Zepu Lu
- research-article
Cache-augmented inbatch importance resampling for training recommender retriever
- Jin Chen
University of Electronic Science and Technology of China
, - Defu Lian
University of Science and Technology of China
, - Yucheng Li
University of Science and Technology of China
, - Baoyun Wang
Hisense
, - Kai Zheng
University of Electronic Science and Technology of China
, - Enhong Chen
University of Science and Technology of China
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems•November 2022, Article No.: 2523, pp 34817-34830Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss. For efficiently training recommender retrievers on ...
- 0Citation
MetricsTotal Citations0- 1
Supplementary Material3600270.3602793_supp.pdf
- Jin Chen
- research-articlePublished By ACMPublished By ACM
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
- Chen Yang
JD.com, Beijing, China
, - Jin Chen
UESTC, Chengdu, China
, - Qian Yu
JD.com, Beijing, China
, - Xiangdong Wu
JD.com, Beijing, China
, - Kui Ma
JD.com, Beijing, China
, - Zihao Zhao
JD.com, Beijing, China
, - Zhiwei Fang
JD.com, Beijing, China
, - Wenlong Chen
JD.com, Beijing, China
, - Chaosheng Fan
JD.com, Beijing, China
, - Jie He
JD.com, Beijing, China
, - Changping Peng
JD.com, Beijing, China
, - Zhangang Lin
JD.com, Beijing, China
, - Jingping Shao
JD.com, Beijing, China
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 4894-4900• https://doi.org/10.1145/3583780.3615456Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for learning large-scale models in industrial scenarios, where only newly ...
- 1Citation
- 175
- Downloads
MetricsTotal Citations1Total Downloads175Last 12 Months93Last 6 weeks5
- Chen Yang
- research-articlePublished By ACMPublished By ACM
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
- Yongfu Fan
University of Electronic Science and Technology of China, Chengdu, China
, - Jin Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Yongquan Jiang
Southwest Jiaotong University, Chengdu, China
, - Defu Lian
University of Science and Technology of China, Hefei, China
, - Fangda Guo
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
, - Kai Zheng
University of Electronic Science and Technology of China, Chengdu, China
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management•October 2023, pp 494-503• https://doi.org/10.1145/3583780.3614789Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items ...
- 0Citation
- 309
- Downloads
MetricsTotal Citations0Total Downloads309Last 12 Months192Last 6 weeks15
- Yongfu Fan
- Article
Cache-Enhanced InBatch Sampling with Difficulty-Based Replacement Strategies for Learning Recommenders
- Yucheng Li
https://ror.org/04c4dkn09University of Science and Technology of China, Hebei, China
, - Defu Lian
https://ror.org/04c4dkn09University of Science and Technology of China, Hebei, China
, - Jin Chen
https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu, China
Database Systems for Advanced Applications. DASFAA 2023 International Workshops•April 2023, pp 95-108• https://doi.org/10.1007/978-3-031-35415-1_7AbstractNegative sampling techniques are prevalent in learning recommenders to reduce the computational cost over the entire corpus, but existing methods still have a significant overhead for re-encoding out-of-batch items. Inbatch sampling is a more ...
- 0Citation
MetricsTotal Citations0
- Yucheng Li
- research-articlePublished By ACMPublished By ACM
RecStudio: Towards a Highly-Modularized Recommender System
- Defu Lian
University of Science and Technology of China, Hefei, China
, - Xu Huang
University of Science and Technology of China, Hefei, China
, - Xiaolong Chen
University of Science and Technology of China, Hefei, China
, - Jin Chen
University of Electronics Science and Technology of China, Chengdu, China
, - Xingmei Wang
University of Science and Technology of China, Hefei, China
, - Yankai Wang
University of Science and Technology of China, Hefei, China
, - Haoran Jin
University of Science and Technology of China, Hefei, China
, - Rui Fan
University of Science and Technology of China, Hefei, China
, - Zheng Liu
Huawei, Beijing, China
, - Le Wu
Hefei University of Technology, Hefei, China
, - Enhong Chen
University of Science and Technology of China, Hefei, China
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2023, pp 2890-2900• https://doi.org/10.1145/3539618.3591894A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms ...
- 2Citation
- 294
- Downloads
MetricsTotal Citations2Total Downloads294Last 12 Months115Last 6 weeks16- 1
Supplementary MaterialSIGIR23-RecStudio-Pre.mp4
- Defu Lian
- research-articlePublished By ACMPublished By ACM
Cooperative Retriever and Ranker in Deep Recommenders
- Xu Huang
University of Science and Technology of China, China
, - Defu Lian
University of Science and Technology of China, China
, - Jin Chen
University of Electronic Science and Technology of China, China
, - Liu Zheng
Microsoft Research Asia, China
, - Xing Xie
Microsoft Research Asia, China
, - Enhong Chen
University of Science and Technology of China, China
WWW '23: Proceedings of the ACM Web Conference 2023•April 2023, pp 1150-1161• https://doi.org/10.1145/3543507.3583422Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small ...
- 5Citation
- 215
- Downloads
MetricsTotal Citations5Total Downloads215Last 12 Months57Last 6 weeks3
- Xu Huang
- research-articlePublished By ACMPublished By ACM
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
- Rui Fan
School of Computer Science, University of Science and Technology of China, China
, - Yuanhao Pu
School of Data Science, University of Science and Technology of China, China
, - Jin Chen
University of Electronic Science and Technology of China, China
, - Zhihao Zhu
School of Data Science, University of Science and Technology of China, China
, - Defu Lian
School of Computer Science, School of Data Science, University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China
, - Enhong Chen
School of Computer Science, School of Data Science, University of Science and Technology of China, China and State Key Laboratory of Cognitive Intelligence, China
WWW '23: Proceedings of the ACM Web Conference 2023•April 2023, pp 1032-1042• https://doi.org/10.1145/3543507.3583349The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and ...
- 2Citation
- 286
- Downloads
MetricsTotal Citations2Total Downloads286Last 12 Months59Last 6 weeks4
- Rui Fan
- research-articlePublished By ACMPublished By ACM
Efficient Learning with Pseudo Labels for Query Cost Estimation
- Shuncheng Liu
University of Electronic Science and Technology of China, Chengdu, China
, - Xu Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Yan Zhao
Aalborg University, Aalborg, Denmark
, - Jin Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Rui Zhou
Huawei Technologies Co., Ltd., Chengdu, China
, - Kai Zheng
University of Electronic Science and Technology of China, Chengdu, China
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management•October 2022, pp 1309-1318• https://doi.org/10.1145/3511808.3557305Query cost estimation, which is to estimate the query plan cost and query execution cost, is of utmost importance to query optimizers. Query plan cost estimation heavily relies on accurate cardinality estimation, and query execution cost estimation ...
- 4Citation
- 294
- Downloads
MetricsTotal Citations4Total Downloads294Last 12 Months42Last 6 weeks5
- Shuncheng Liu
- research-articlePublished By ACMPublished By ACM
Efficient Join Order Selection Learning with Graph-based Representation
- Jin Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Guanyu Ye
University of Electronic Science and Technology of China, Chengdu, China
, - Yan Zhao
Aalborg University, Aalborg, China
, - Shuncheng Liu
University of Electronic Science and Technology of China, Chengdu, China
, - Liwei Deng
University of Electronic Science and Technology of China, Chengdu, China
, - Xu Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Rui Zhou
Huawei Technologies Co., Ltd., Chengdu, China
, - Kai Zheng
University of Electronic Science and Technology of China, Chengdu, China
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 97-107• https://doi.org/10.1145/3534678.3539303Join order selection plays an important role in DBMS query optimizers. The problem aims to find the optimal join order with the minimum cost, and usually becomes an NP-hard problem due to the exponentially increasing search space. Recent advanced studies ...
- 13Citation
- 651
- Downloads
MetricsTotal Citations13Total Downloads651Last 12 Months129Last 6 weeks15- 1
Supplementary MaterialKDD-fp0821.mp4
- Jin Chen
- research-articlePublished By ACMPublished By ACM
Improving Implicit Alternating Least Squares with Ring-based Regularization
- Rui Fan
University of Science and Technology of China, Hefei, China
, - Jin Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Jin Zhang
University of Science and Technology of China, Hefei, China
, - Defu Lian
University of Science and Technology of China, Hefei, China
, - Enhong Chen
University of Science and Technology of China, Hefei, China
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2022, pp 102-111• https://doi.org/10.1145/3477495.3531995Due to the widespread presence of implicit feedback, recommendation based on them has been a long-standing research problem in academia and industry. However, it suffers from the extremely-sparse problem, since each user only interacts with a few items. ...
- 1Citation
- 288
- Downloads
MetricsTotal Citations1Total Downloads288Last 12 Months31Last 6 weeks1- 1
Supplementary MaterialSIGIR22-modfp0847.mp4
- Rui Fan
- research-articlePublished By ACMPublished By ACM
Learning Recommenders for Implicit Feedback with Importance Resampling
- Jin Chen
University of Electronic Science and Technology of China, China
, - Defu Lian
University of Science and Technology of China, China
, - Binbin Jin
Huawei Cloud Computing Technologies Co., Ltd., China
, - Kai Zheng
University of Electronic Science and Technology of China, China
, - Enhong Chen
University of Science and Technology of China, China
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 1997-2005• https://doi.org/10.1145/3485447.3512075Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers from the lack of negative samples, which has a significant impact on the training of recommendation models. Existing negative sampling is based on the static ...
- 14Citation
- 523
- Downloads
MetricsTotal Citations14Total Downloads523Last 12 Months78Last 6 weeks7
- Jin Chen
- research-articlePublished By ACMPublished By ACM
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
- Jin Chen
University of Electronic Science and Technology of China, China and University of Science and Technology of China, China
, - Defu Lian
University of Science and Technology of China, China
, - Binbin Jin
Huawei Cloud Computing Technologies Co., Ltd., China
, - Xu Huang
University of Science and Technology of China, China
, - Kai Zheng
University of Electronic Science and Technology of China, China
, - Enhong Chen
University of Science and Technology of China, China
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 1944-1954• https://doi.org/10.1145/3485447.3512068Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over all items, such that it takes linear costs in the number of items to ...
- 21Citation
- 300
- Downloads
MetricsTotal Citations21Total Downloads300Last 12 Months48Last 6 weeks1
- Jin Chen
- research-articlePublished By ACMPublished By ACM
xLightFM: Extremely Memory-Efficient Factorization Machine
- Gangwei Jiang
University of Science and Technology of China, Hefei, China
, - Hao Wang
University of Science and Technology of China, Hefei, China
, - Jin Chen
University of Electronic Science and Technology of China, Chengdu, China
, - Haoyu Wang
SUNY Buffalo, Buffalo, NY, USA
, - Defu Lian
University of Science and Technology of China, Hefei, China
, - Enhong Chen
University of Science and Technology of China, Hefei, China
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2021, pp 337-346• https://doi.org/10.1145/3404835.3462941The factorization-based models have achieved great success in online advertisements and recommender systems due to the capability of efficiently modeling combinational features. These models encode feature interactions by the vector product between ...
- 13Citation
- 514
- Downloads
MetricsTotal Citations13Total Downloads514Last 12 Months62Last 6 weeks6- 1
Supplementary Materialtotal.mp4
- Gangwei Jiang
- research-articlePublished By ACMPublished By ACM
Automated Creative Optimization for E-Commerce Advertising
- Jin Chen
University of Electronic Science and Technology of China, China
, - Ju Xu
Alibaba Group, China
, - Gangwei Jiang
University of Science and Technology of China, China
, - Tiezheng Ge
Alibaba Group, China
, - Zhiqiang Zhang
Alibaba Group, China
, - Defu Lian
University of Science and Technology of China, China
, - Kai Zheng
University of Electronic Science and Technology of China, China
WWW '21: Proceedings of the Web Conference 2021•April 2021, pp 2304-2313• https://doi.org/10.1145/3442381.3449909Advertising creatives are ubiquitous in E-commerce advertisements and aesthetic creatives may improve the click-through rate (CTR) of the products. Nowadays smart advertisement platforms provide the function of compositing creatives based on source ...
- 9Citation
- 380
- Downloads
MetricsTotal Citations9Total Downloads380Last 12 Months63Last 6 weeks6
- Jin Chen
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.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
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.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- 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