[AISWorld] CFP: International Workshop on Explainable AI for Recommender System

[xiangmin zhang]

*International Workshop on Explainable AI forRecommender Systems*
*with The 21st IEEE/WIC/ACM International Conference on Web Intelligence
and Intelligent Agent Technology(WIC = Artificial Intelligence in the
Connected World)*

November 17-20, 2022, Niagara Falls, Canada
A Hybrid Conference with both Online and Offline Modes
Conference web page: https://www.wi-iat.com/wi-iat2022/index.html

Introduction:
With the rise of various e-commerce, online video sharing & social media
platforms, and many other web services, recommender systems have attracted
more and more attention in the past few decades. Recommender systems aim to
suggest relevant items to users by studying past behaviors, preferences,
ratings and other relevant data. Despite the rapid advances of recommender
systems recently, the application of “black-box” decision mechanisms to
recommender systems has become one of the key challenges, lacking
explainability
and interpretability. This is especially problematic for downstream tasks in
industries such as healthcare, manufacturing, insurance, and autonomous
vehicles.
Explainable artificial intelligence (XAI), which refers to a set of methods
that empower the decision-making process with accuracy, transparency and
fairness while allowing users/ system designers to understand and trust the
results generated by machine learning algorithms, is a possible solution to
tackle the problems stated above.
By bringing these two concepts together, the aim of this workshop is to
engage top-tier researchers from recommender systems and explainable AI
communities, to combine perspectives across different domains, to deliver
the state-of-the-art research insights, and to tackle the existing and
foreseeable challenges together. It will focus on various applications of
explainable AI techniques to recommender systems and different use cases.
For example, we can target the human-computer interaction perspective
of explainable
recommendations with explainable information sources and display
explanation format. We can also target different machine learning models,
including but are not limited to topic modeling, graph-based models, deep
learning-based models and knowledge graph-based approaches, that generate
explainability for recommender systems. We can also extend explainable AI
to different downstream recommendation tasks, such as e-commerce, social
media, financial product recommendations and medical recommendations. This
workshop will present a stage for researchers to showcase their research
on the advancement and the next generation of explainable recommender
systems, thus promoting human-in-the-loop AI applications and bringing
high-quality
research results to the common public.

Topics of interest:
We solicit original contributions developing explainable AI for recommender
systems, including but are not limited to the following topics:

- Explainable models that deliver persuasive explanations on model outputs,
and/or generate faithful interpretations to reflect and justify the decision
-making process
- Using Explainable AI to identify bias in recommender systems
- Explainable recommender systems with low-quality data and/or uncertainties
- Explainability and Human-in-the-Loop development of AI in recommender
systems
- Explainable AI to support interactive recommender systems
- Presentation and personalization of AI explanations to the recommendation
results for different target groups
- Privacy-awared recommender systems, including but are not limited to
federated learning, privacy protection mechanisms for ranking.
- Explainable AI for transparency, fairness and unbiased decision making in
recommender systems
- The recommender system developers’ perspective on explainable AI
- The recommender system users’ perspective on explainable AI
- Surveys, evaluations or benchmarking on the state-of-the-art research in
the area of explainable recommender systems

*Important Dates:*
● Submission deadline: Aug 20th , 2022
● Acceptance notification: Oct 1st , 2022

Submission Information:
https://wi-
lab.com/cyberchair/2022/wi22/scripts/submit.php?subarea=S17&undisplay_detail=1&wh=/cyberchair/2
022/wi22/scripts/ws_submit.php

Workshop Chair
Sherry Zhu, AI Singapore, Singapore sherryzhu0309 at gmail.com ;
sherryzhu at aisingapore.org

Workshop Co-Chairs
Xiangmin Zhang, Wayne State University, ae9101 at wayne.edu
Aixin Sun, Nanyang Technological University, axsun at ntu.edu.sg
Zhiwen Xie, Wuhan University, xiezhiwen at whu.edu.cn

Should you have any questions or concerns, please do not hesitate to email
to
sherryzhu0309 at gmail.com or xiezhiwen at whu.edu.cn. Thanks!
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