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IRS-Frontiers 2022 : Frontiers in Big Data - Industrial Recommender Systems

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Link: https://www.frontiersin.org/research-topics/29293/industrial-recommender-systems
 
When N/A
Where N/A
Abstract Registration Due Feb 16, 2022
Submission Deadline Mar 16, 2022
Categories    recommender systems   big data
 

Call For Papers

CFP for the Frontiers in Big Data for research topic Industrial Recommender Systems

Call for papers
Frontiers in Big Data - Industrial Recommender Systems
https://www.frontiersin.org/research-topics/29293/industrial-recommender-systems
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Key Dates

Abstract submission: 16 Feb 2022
Paper submission: 16 March 2022

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Description

Recommendation systems are used widely across many industries, such as ecommerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect; thus, improving the user experience. This motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. These key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia’s contributions for industrial recommendation systems. This special issue aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.

The gap between the practitioners and academia researchers on industrial recommendation systems has not been widely recognized or effectively addressed, given that there have been numerous conferences with topics focusing on or including recommendation systems, such as ICLR, RecSys, ICDM, SDM, and CIKM. With the rapid development of the recommendation areas, the requirement becomes more and more urgent to (i) attract more researchers from different areas on industrial recommendation systems, and (ii) bring up the pain points in industry so that academia researchers can pay more attention and build connections with practitioners. With the reputation of Frontiers in Big Data, it could be expected that this special issue will draw a lot of interest from the research community and practitioners alike.


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Topics

This journal welcomes submissions from researchers and industrial practitioners broadly related to recommendation systems, such as novel recommendation models, efficient recommendation algorithms, novel industrial frameworks, etc. In order to emphasize the gap between the two communities, we extremely welcome submissions on industrial recommendation system infrastructures based on given resources, models and algorithms supported by the specific infrastructures, and frameworks or end-to-end systems that have been deployed in real world production.

Specific topics of interest are including but not limited to:

1. Frameworks or end-to-end systems from industry are extremely welcomed.
2. Scalable Recommender systems.
3. Personalization, including personalized product recommendation, streaming content
4. recommendation, ads recommendation, news and article recommendation, etc.
5. New applications related to recommendation systems.
6. Existing or novel infrastructures for recommendation systems.
7. Interactive recommendation system
8. Explainability of recommendations.
9. Fairness, privacy and security in recommender systems.
10. Recommendations under multi-objective and constraints.
11. Reproducibility of models and evaluation metrics.
12. Unbiased recommendation.
13. User research studies on real-world recommender systems.
14. Business impact of recommendation systems.

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Submission Directions
The journal accepts different formats of article types with different limits on word count. For more details on different types please see here (https://www.frontiersin.org/journals/big-data#article-types). The additional author guidelines are here (https://www.frontiersin.org/journals/big-data#author-guidelines).
The submission fee will depend on article types and is listed on https://www.frontiersin.org/about/publishing-fees under section Article Types. For example, Original Research articles are A-type articles, Brief Research Reports are B-Type articles and Data Reports are C-Type articles. We also have fee support for authors through Frontiers institutional memberships and Frontiers Fee Support Program.


Reviews are not double-blind, and author names and affiliations should be listed. Manuscript formatting guidelines, including latex templates are here (https://www.frontiersin.org/about/author-guidelines). Manuscripts that do not meet the formatting requirements will be rejected without review.
For details of submission, please check the website of the journal: https://www.frontiersin.org/research-topics/29293/industrial-recommender-systems. Please reach out to irs-kdd@googlegroups.com for any questions.

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