Call for papers 

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2nd International Workshop on Industrial Recommendation Systems

To be held in conjunction with KDD 2021

August 14th-18th, 2021,

https://irsworkshop.github.io/2021/


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Key Dates

Workshop paper and poster submissions: May 10, 2021

Workshop paper and poster notifications: June 10, 2021

Camera-ready deadline: July 10, 2021  

Workshop date: August 14, 2021

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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 workshop 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, CIKM, to name a few. With the fast development of related research 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. This workshop attracted great interest from audiences on KDD2020 (being highlighted as the top 5 events with extraordinary excitement from audience, over 42 KDD2020 Day 2 events), with a 12.5% acceptance rate for oral presentation, all from reputable industry companies. 


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Topics

This workshop 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: 


* Frameworks or end-to-end systems from industry are extremely welcomed.

* Scalable Recommender systems.

* Personalization, including personalized product recommendation, streaming content recommendation, ads recommendation, news and article recommendation, etc. 

* New applications related to recommendation systems. 

* Existing or novel infrastructures for recommendation systems. 

* Interactive recommendation system

* Explainability of recommendations.

* Fairness, privacy and security in recommender systems.

* Recommendations under multi-objective and constraints.

* Reproducibility of models and evaluation metrics.

* Unbiased recommendation.

* User research studies on real-world recommender systems.

* Business impact of recommendation systems. 


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Submission Directions

The workshop accepts long papers (limited to 9 pages), short papers (6 pages), posters (4 pages), abstracts and demos (2 pages). Paper submission and reviewing will be following the directions of the KDD main conference. Reviews are not double-blind, and author names and affiliations should be listed. Submissions should include all content and references within the limited pages, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. For LaTeX users: unzip acmart.zip, make, and use sample-sigconf.tex as a template. Additional information about formatting and style files is available online at: https://www.acm.org/publications/proceedings-template. Papers that do not meet the formatting requirements will be rejected without review. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. 

For details of submission, please check the website of the workshop: https://irsworkshop.github.io/2021/. Please submit your paper through this Easychair link (https://easychair.org/conferences/?conf=irs20210). Please reach out to irs-kdd@googlegroups.com or irs2021-0@easychair.org for any questions.


Additional information and contact details 
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https://irsworkshop.github.io/2021/
irs-kdd@googlegroups.com