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活动简介

ACM UMAP is the premier international conference for researchers and practitioners

working on systems that adapt to individual users or groups of users, and that

collect, represent, and model user information. ACM UMAP  is sponsored by ACM

SIGCHI and SIGWEB. User Modeling Inc., as the core Steering Committee, oversees

the conference organization. The proceedings, published by ACM, will be part of the

ACM Digital Library.

 

The theme of UMAP 2023 is "Personalization in Times of Crisis”. Specifically, we

welcome submissions that highlight the impact that critical periods (such as the

COVID-19 pandemic, ongoing wars, and climate change, to name a few) can have on

user modeling, personalization, and adaptation of (intelligent) systems; the focus is

on investigations that capture how these trying times may have influenced user

behavior and whether new models are required. 

 

While we encourage submissions related to this theme, the scope of the conference

is not limited to the theme only. As always, contributions from academia, industry,

and other organizations discussing open challenges or novel research approaches

are expected to be supported by rigorous evidence appropriate to the claims (e.g.,

user study, system evaluation, computational analysis).

组委会

Program Chairs

 

• Julia Neidhardt, TU Wien, Austria 

• Sole Pera, TU Delft, The Netherlands    

征稿信息

重要日期

2023-01-19
摘要截稿日期
2023-01-26
初稿截稿日期
2023-04-11
初稿录用日期
2023-05-02
终稿截稿日期

ACM UMAP is the premier international conference for researchers and practitioners

working on systems that adapt to individual users or groups of users, and that

collect, represent, and model user information. ACM UMAP  is sponsored by ACM

SIGCHI and SIGWEB. User Modeling Inc., as the core Steering Committee, oversees

the conference organization. The proceedings, published by ACM, will be part of the

ACM Digital Library.

 

The theme of UMAP 2023 is "Personalization in Times of Crisis”. Specifically, we

welcome submissions that highlight the impact that critical periods (such as the

COVID-19 pandemic, ongoing wars, and climate change, to name a few) can have on

user modeling, personalization, and adaptation of (intelligent) systems; the focus is

on investigations that capture how these trying times may have influenced user

behavior and whether new models are required. 

 

While we encourage submissions related to this theme, the scope of the conference

is not limited to the theme only. As always, contributions from academia, industry,

and other organizations discussing open challenges or novel research approaches

are expected to be supported by rigorous evidence appropriate to the claims (e.g.,

user study, system evaluation, computational analysis).

征稿范围

Conference Topics

 

We welcome submissions related to user modeling, personalization, and adaptation

of (intelligent) systems targeting a broad range of users and domains. Detailed

descriptions and the suggested topics for each track will be available shortly in the

extended version of the CFP on the UMAP 2023 website.

 

Personalized Recommender Systems

This track invites works from researchers and practitioners on recommender

systems. In addition to mature research works addressing technical aspects of

recommendations, we welcome research contributions that address questions

related to user perception, decision-making, and the business value of

recommender systems.

 

Adaptive, Semantic, Knowledge, and Social Graphs

This track welcomes works focused on the use of knowledge representations (i.e.,

novel knowledge bases), graph algorithms (i.e., graph embedding techniques), and

social network analysis at the service of addressing all aspects of personalization,

user model building, and personal experience in online social systems. Moreover,

this track invites works in adaptive hypermedia, as well as semantic and social web.

 

Intelligent User Interfaces

This track invites works exploring how to make the interaction between computers

and people smarter and more productive, leveraging solutions from

human-computer interaction, data mining, natural language processing,

information visualization, and knowledge representation and reasoning.

 

Personalizing Learning Experiences through User Modeling

This track invites researchers, developers, and practitioners from various disciplines

to submit their innovative learning solutions, share acquired experiences, and

discuss their modeling challenges for personalized adaptive learning.

 

Fairness, Transparency, Accountability, and Privacy

Researchers, developers, and practitioners have a social responsibility to account for

the impact that technologies have on individuals (users, providers, and other

stakeholders) and society. This track invites works related to the science of building,

maintaining, evaluating, and studying adaptive systems that are fair, transparent,

respectful of users’ privacy, beneficial to society, and accountable for their impacts.

 

Personalization for Persuasive and Behavior Change Systems

This track invites submissions focused on personalization and tailoring for

persuasive technologies, including but not limited to personalization models, user

models, computational personalization, design, and evaluation methods. It also

welcomes work that brings attention to the user experience and designing

personalized and adaptive behavior change technologies.

 

Virtual Assistants, Conversational Interactions, and Personalized Human-robot

Interaction

This track invites works investigating new models and techniques for adapting

synthetic companions (e.g., virtual assistants, chatbots, social robots) to individual

users. With the conversational modality so in vogue across disciplines, this track

welcomes work highlighting the model and deployment of synthetic companions

driven by conversational search and recommendation paradigms.

 

Research Methods and Reproducibility

This track invites submissions on methodologies to evaluate personalized systems,

benchmarks, and measurement scales, with particular attention to the reproducibility

of results and techniques. Furthermore, the track looks for submissions that report

new insights from reproducing existing works.

作者指南

Submission and Review Process

 

Submissions for any of the aforementioned tracks should have a maximum length of

*14 pages* (excluding references) in the ACM new single-column format

(https://www.acm.org/publications/proceedings-template). (Papers of any length up

to 14 pages are encouraged; reviewers will comment on whether the size is

appropriate for the contribution.)  Additional review criteria and submission link will

be available shortly on the conference website: https://www.um.org/umap2023/ .

 

Accepted papers will be included in the conference proceedings and presented at the

conference. At least one author should register for the conference by the early

registration date cut-off.

 

UMAP uses a *double-blind* review process. Authors must omit their names and

affiliations from their submissions; they should also avoid obvious identifying

statements. For instance, citations to the authors' prior work should be in the third

person. Submissions not abiding by anonymity requirements will be desk rejected.  

 

UMAP has a *no dual submission* policy, which is why full paper submissions should

not be currently under review at another publication venue. Further, UMAP operates

under the ACM Conference Code of Conduct

(https://www.acm.org/about-acm/policy-against-harassment).

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重要日期
  • 会议日期

    06月26日

    2023

    06月29日

    2023

  • 01月19日 2023

    摘要截稿日期

  • 01月26日 2023

    初稿截稿日期

  • 04月11日 2023

    初稿录用通知日期

  • 05月02日 2023

    终稿截稿日期

主办单位
ACM
承办单位
SEIT Lab, Department of Computer Science, University of Cyprus
协办单位
User Modeling Inc
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