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- research-articleOpen AccessPublished By ACMPublished By ACM
Designing Expressive and Liquid Financial Options Markets via Linear Programming and Automated Market Making
- Xintong Wang
Rutgers University, US
, - David M. Pennock
Rutgers University, US
, - David M. Rothschild
Microsoft Research, US
, - Nikhil R. Devanur
Amazon, US
ICAIF '24: Proceedings of the 5th ACM International Conference on AI in Finance•November 2024, pp 496-503• https://doi.org/10.1145/3677052.3698687We propose a two-stage market making strategy to improve the expressivity and liquidity of financial options markets, supporting investors to trade options of any strike price at the most competitive prices. In the first stage, we formulate a linear ...
- 0Citation
- 131
- Downloads
MetricsTotal Citations0Total Downloads131Last 12 Months131Last 6 weeks75- 1
Supplementary MaterialICAIF_24_appendix.pdf
- Xintong Wang
- research-articleOpen AccessPublished By ACMPublished By ACM
Accuracy and Fairness for Web-Based Content Analysis under Temporal Shifts and Delayed Labeling
- Abdulaziz A. Almuzaini
Department of Computer Science, Rutgers University, United States
, - David M. Pennock
Department of Computer Science, Rutgers University, United States
, - Vivek K. Singh
School of Communication and Information, Rutgers University, United States
WEBSCI '24: Proceedings of the 16th ACM Web Science Conference•May 2024, pp 268-278• https://doi.org/10.1145/3614419.3644028Web-based content analysis tasks, such as labeling toxicity, misinformation, or spam often rely on machine learning models to achieve cost and scale efficiencies. As these models impact real human lives, ensuring accuracy and fairness of such models is ...
- 0Citation
- 227
- Downloads
MetricsTotal Citations0Total Downloads227Last 12 Months227Last 6 weeks65- 1
Supplementary MaterialPaperSession-5_Hate_Speech_einzeln_Donnerstag_240605_AbdulazizAlmuzaini.mp4
- Abdulaziz A. Almuzaini
- research-article
An Equivalence Between Fair Division and Wagering Mechanisms
- Rupert Freeman
Darden School of Business, University of Virginia, Charlottesville, Virginia 22903;
, - Jens Witkowski
Frankfurt School of Finance & Management, 60322 Frankfurt, Germany;
, - Jennifer Wortman Vaughan
Microsoft Research, New York, New York 10012;
, - David M. Pennock
Department of Computer Science, Rutgers University, New Brunswick, New Jersey 08854
Management Science, Volume 70, Issue 10•October 2024, pp 6704-6723 • https://doi.org/10.1287/mnsc.2022.02615We draw a surprising and direct mathematical equivalence between the class of fair division mechanisms, designed to allocate divisible goods without money, and the class of weakly budget-balanced wagering mechanisms, designed to elicit probabilities. ...
- 0Citation
MetricsTotal Citations0
- Rupert Freeman
- posterPublic Access
Artificial Prediction Markets Present a Novel Opportunity for Human-AI Collaboration
- Tatiana Chakravorti
Pennsylvania State University, State College, PA, USA
, - Vaibhav Singh
Pennsylvania State University, State College, PA, USA
, - Sarah Rajtmajer
Pennsylvania State University, State College, PA, USA
, - Michael McLaughlin
Pennsylvania State University, State College, PA, USA
, - Robert Fraleigh
Pennsylvania State University, State College, PA, USA
, - Christopher Griffin
Pennsylvania State University, State College, PA, USA
, - Anthony Kwasnica
Pennsylvania State University, State College, PA, USA
, - David Pennock
Rutgers University, Piscataway, NJ, USA
, - C. Lee Giles
Pennsylvania State University, State College, PA, USA
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems•May 2023, pp 2304-2306Despite high-profile successes in the field of Artificial Intelligence, machine-driven technologies still suffer important limitations, particularly for complex tasks where creativity, planning, common sense, intuition, or learning from limited data is ...
- 0Citation
- 76
- Downloads
MetricsTotal Citations0Total Downloads76Last 12 Months55Last 6 weeks22
- Tatiana Chakravorti
- research-articleOpen Access
Incentive-Compatible Forecasting Competitions
- Jens Witkowski
Frankfurt School of Finance & Management, Frankfurt 60322, Germany;
, - Rupert Freeman
Darden School of Business, University of Virginia, Charlottesville, Virginia 22903;
, - Jennifer Wortman Vaughan
Microsoft Research, New York, New York 10012;
, - David M. Pennock
Department of Computer Science, Rutgers University, New Brunswick, New Jersey 08854;
, - Andreas Krause
Department of Computer Science, ETH Zurich, Zurich 8092, Switzerland
Management Science, Volume 69, Issue 3•March 2023, pp 1354-1374 • https://doi.org/10.1287/mnsc.2022.4410We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We have two objectives: (1) to incentivize forecasters to report truthfully and (...
- 0Citation
MetricsTotal Citations0
- Jens Witkowski
- research-articlePublished By ACMPublished By ACM
ABCinML: Anticipatory Bias Correction in Machine Learning Applications
- Abdulaziz A. Almuzaini
Rutgers University, USA
, - Chidansh A. Bhatt
IBM, USA
, - David M. Pennock
Rutgers University, USA
, - Vivek K. Singh
Rutgers University, USA
FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency•June 2022, pp 1552-1560• https://doi.org/10.1145/3531146.3533211The idealization of a static machine-learned model, trained once and deployed forever, is not practical. As input distributions change over time, the model will not only lose accuracy, any constraints to reduce bias against a protected class may fail to ...
- 4Citation
- 390
- Downloads
MetricsTotal Citations4Total Downloads390Last 12 Months52Last 6 weeks9
- Abdulaziz A. Almuzaini
- research-articlePublished By ACMPublished By ACM
Designing a Combinatorial Financial Options Market
- Xintong Wang
Harvard University, Cambridge, MA, USA
, - David M. Pennock
Rutgers University, New Brunswick, NJ, USA
, - Nikhil R. Devanur
Amazon, Seattle, WA, USA
, - David M. Rothschild
Microsoft Research, New York City, NY, USA
, - Biaoshuai Tao
Shanghai Jiao Tong University, Shanghai, China
, - Michael P. Wellman
University of Michigan, Ann Arbor, MI, USA
EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation•July 2021, pp 864-883• https://doi.org/10.1145/3465456.3467634Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price by an expiration date. Standard exchanges offer options of predetermined strike values and trade options of different strikes independently, even ...
- 2Citation
- 150
- Downloads
MetricsTotal Citations2Total Downloads150Last 12 Months25Last 6 weeks2- 1
Supplementary Materialec0484faux.zip
- Xintong Wang
- research-article
Log-time Prediction Markets for Interval Securities
- Miroslav Dudík
Microsoft Research, New York City, NY, USA
, - Xintong Wang
University of Michigan, Ann Arbor, MI, USA
, - David M. Pennock
Rutgers University, New Brunswick, NJ, USA
, - David M. Rothschild
Microsoft Research, New York City, NY, USA
AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems•May 2021, pp 465-473We design a prediction market to recover a complete and fully general probability distribution over a random variable. Traders buy and sellinterval securities that pay $1 if the outcome falls into an interval and $0 otherwise. Our market takes the form ...
- 1Citation
- 24
- Downloads
MetricsTotal Citations1Total Downloads24Last 12 Months2
- Miroslav Dudík
- research-article
Beating greedy for approximating reserve prices in multi-unit VCG auctions
- Mahsa Derakhshan
University of Maryland
, - David M. Pennock
Rutgers University
, - Aleksandrs Slivkins
Microsoft Research
SODA '21: Proceedings of the Thirty-Second Annual ACM-SIAM Symposium on Discrete Algorithms•January 2021, pp 1099-1118We study the problem of finding personalized reserve prices for unit-demand buyers in multi-unit eager VCG auctions with correlated buyers. The input to this problem is a dataset of submitted bids of n buyers in a set of auctions. The goal is to find a ...
- 0Citation
- 20
- Downloads
MetricsTotal Citations0Total Downloads20Last 12 Months1
- Mahsa Derakhshan
- research-articlefree
Proportionality in approval-based elections with a variable number of winners
- Rupert Freeman
Microsoft Research New York City
, - Anson Kahng
Carnegie Mellon University
, - David M. Pennock
Rutgers University
IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence•January 2021, Article No.: 19, pp 132-138We study proportionality in approval-based multiwinner elections with a variable number of winners, where both the size and identity of the winning committee are informed by voters' opinions. While proportionality has been studied in multiwinner elections ...
- 0Citation
- 84
- Downloads
MetricsTotal Citations0Total Downloads84Last 12 Months76Last 6 weeks10
- Rupert Freeman
- research-articlePublished By ACMPublished By ACM
The Possibilities and Limitations of Private Prediction Markets
- Rachel Cummings
Georgia Institute of Technology, Atlanta, GA
, - David M. Pennock
Microsoft Research, New York, NY
, - Jennifer Wortman Vaughan
Microsoft Research, New York, NY
ACM Transactions on Economics and Computation, Volume 8, Issue 3•August 2020, Article No.: 15, pp 1-24 • https://doi.org/10.1145/3412348We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants’ actions or beliefs. Our goal is to design market mechanisms ...
- 0Citation
- 99
- Downloads
MetricsTotal Citations0Total Downloads99Last 12 Months14Last 6 weeks1
- Rachel Cummings
- research-articlefree
No-regret and incentive-compatible online learning
- Rupert Freeman
Darden School of Business, University of Virginia
, - David M. Pennock
DIMACS, Rutgers University
, - Chara Podimata
Harvard University
, - Jennifer Wortman Vaughan
Microsoft Research NYC
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 306, pp 3270-3279We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the ...
- 0Citation
- 62
- Downloads
MetricsTotal Citations0Total Downloads62Last 12 Months49Last 6 weeks5
- Rupert Freeman
- research-article
Channel Auctions
- Eduardo M. Azevedo
The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;
, - David M. Pennock
Microsoft Research, Redmond, Washington 98052;
, - Bo Waggoner
University of Colorado, Boulder, Colorado 80303
, - E. Glen Weyl
Microsoft Research, Redmond, Washington 98052;
Management Science, Volume 66, Issue 5•May 2020, pp 2075-2082 • https://doi.org/10.1287/mnsc.2019.3487Standard auction formats feature either an upper bound on the equilibrium price that descends over time (as in the Dutch auction) or a lower bound on the equilibrium price that ascends over time (as in the English auction). We show that in some settings ...
- 1Citation
MetricsTotal Citations1
- Eduardo M. Azevedo
- extended-abstractPublished By ACMPublished By ACM
Truthful Aggregation of Budget Proposals
- Rupert Freeman
Microsoft Research, New York, NY, USA
, - David M. Pennock
Microsoft Research, New York, NY, USA
, - Dominik Peters
University of Oxford, Oxford, United Kingdom
, - Jennifer Wortman Vaughan
Microsoft Research, New York, NY, USA
EC '19: Proceedings of the 2019 ACM Conference on Economics and Computation•June 2019, pp 751-752• https://doi.org/10.1145/3328526.3329557We study a participatory budgeting setting in which a single divisible resource (such as money or time) must be divided among a set of projects. For example, participatory budgeting could be used to decide how to divide a city's tax surplus between its ...
- 12Citation
- 260
- Downloads
MetricsTotal Citations12Total Downloads260Last 12 Months53Last 6 weeks2- 1
Supplementary Materialp751-freeman.mp4
- Rupert Freeman
- research-articlePublished By ACMPublished By ACM
Smarter Markets: Bringing Intelligence into the Exchange
- David Pennock
Microsoft Research
WWW '19: Companion Proceedings of The 2019 World Wide Web Conference•May 2019, pp 930-930• https://doi.org/10.1145/3308560.3316530Billions of dollars in financial securities exchange hands every day in independent continuous double auctions. Although the auctions are automated, fast, open 24-7, and have worldwide scope and massive scale, the underlying auction rules have not ...
- 0Citation
- 82
- Downloads
MetricsTotal Citations0Total Downloads82
- David Pennock
- research-articlefree
An equivalence between wagering and fair-division mechanisms
- Rupert Freeman
Microsoft Research
, - David M. Pennock
Microsoft Research
, - Jennifer Wortman Vaughan
Microsoft Research
AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence•January 2019, Article No.: 241, pp 1957-1964• https://doi.org/10.1609/aaai.v33i01.33011957We draw a surprising and direct mathematical equivalence between the class of allocation mechanisms for divisible goods studied in the context of fair division and the class of weakly budget-balanced wagering mechanisms designed for eliciting ...
- 0Citation
- 45
- Downloads
MetricsTotal Citations0Total Downloads45Last 12 Months24Last 6 weeks6
- Rupert Freeman
- article
Fair allocation of indivisible goods to asymmetric agents
- Alireza Farhadi
University of Maryland, College Park, Maryland
, - Mohammad Ghodsi
Sharif University of Technology, IPM, Institute for Research in Fundamental Sciences, Tehran, Iran
, - MohammadTaghi Hajiaghayi
University of Maryland, College Park, Maryland
, - Sébastien Lahaie
Google Research, New York City
, - David Pennock
Microsoft Research, New York City
, - Masoud Seddighin
Sharif University of Technology, Tehran, Iran
, - Saeed Seddighin
University of Maryland, College Park, Maryland
, - Hadi Yami
University of Maryland, College Park, Maryland
Journal of Artificial Intelligence Research, Volume 64, Issue 1•January 2019, pp 1-20 • https://doi.org/10.1613/jair.1.11291We study fair allocation of indivisible goods to agents with unequal entitlements. Fair allocation has been the subject of many studies in both divisible and indivisible settings. Our emphasis is on the case where the goods are indivisible and agents ...
- 30Citation
MetricsTotal Citations30
- Alireza Farhadi
- research-articlePublished By ACMPublished By ACM
A combinatorial prediction market for the U.S. elections
- Miroslav Dudik
Microsoft Research, New York, NY, USA
, - Sebastien Lahaie
Microsoft Research, New York, NY, USA
, - David M. Pennock
Microsoft Research, New York, NY, USA
, - David Rothschild
Microsoft Research, New York, NY, USA
EC '13: Proceedings of the fourteenth ACM conference on Electronic commerce•June 2013, pp 341-358• https://doi.org/10.1145/2482540.2482601We report on a large-scale case study of a combinatorial prediction market. We implemented a back-end pricing engine based on Dudik et al.'s (2012) combinatorial market maker, together with a wizard-like front end to guide users to constructing any of ...
- 2Citation
- 16
- Downloads
MetricsTotal Citations2Total Downloads16Last 12 Months2
- Miroslav Dudik
- research-articlePublished By ACMPublished By ACM
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
- Hoda Heidari
ETH Zürich, Zurich, Switzerland
, - Sébastien Lahaie
Google Research, New York, USA
, - David M. Pennock
Microsoft Research, New York, USA
, - Jennifer Wortman Vaughan
Microsoft Research, New York, USA
ACM Transactions on Economics and Computation, Volume 6, Issue 3-4•November 2018, Article No.: 15, pp 1-26 • https://doi.org/10.1145/3274643We provide the first concrete algorithm for combining market makers and limit orders in a prediction market with continuous trade. Our mechanism is general enough to handle both bundle orders and arbitrary securities defined over combinatorial outcome ...
- 1Citation
- 242
- Downloads
MetricsTotal Citations1Total Downloads242Last 12 Months10Last 6 weeks2
- Hoda Heidari
- Article
An axiomatic view of the parimutuel consensus wagering mechanism
- Rupert Freeman
Duke University
, - David M. Pennock
Microsoft Research
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence•July 2018, pp 254-260We consider an axiomatic view of the Parimutuel Consensus Mechanism (PCM) defined by Eisenberg and Gale [1959]. The parimutuel consensus mechanism can be interpreted as a parimutuel market for wagering with a proxy that bets optimally on behalf of the ...
- 0Citation
MetricsTotal Citations0
- Rupert Freeman
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