[AISWorld] Updated CFP: International Workshop on Explainable AI for Recommender Systems (with the opportunity of being included in a special issue of Computational Intelligence)

[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: *
>
> *Accepted papers of this workshop will also be invited  to publish an
> extended version in a special issue of the Computational Intelligence: An
> International Journal. *
>
>
> *https://wi-lab.com/cyberchair/2022/wi22/scripts/submit.php?subarea=S17&undisplay_detail=1&wh=/cyberchair/2022/wi22/scripts/ws_submit.php*
> <https://wi-lab.com/cyberchair/2022/wi22/scripts/submit.php?subarea=S17&undisplay_detail=1&wh=/cyberchair/2022/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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