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IRS 2020 : 1st International Workshop on Industrial Recommendation Systems (conjunction with KDD 2020)

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Link: https://irsworkshop.github.io/2020/index.html
 
When Aug 24, 2020 - Aug 24, 2020
Where San Diego, CA
Submission Deadline May 31, 2020
Notification Due Jun 15, 2020
Final Version Due Jul 1, 2020
Categories    recommender systems   machine learning
 

Call For Papers

Call for papers
1st International Workshop on Industrial Recommendation Systems
To be held in conjunction with KDD 2020
August 24, 2020, San Diego Convention Center, San Diego, CA
https://irsworkshop.github.io/2020/

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Key Dates

Workshop paper and poster submissions: May 31, 2020 (Extended from May 20, 2020)
Workshop paper and poster notifications: June 15, 2020
Workshop date: August 24, 2020
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Description

Recommendation systems are used widely in ecommerce industries and multimedia content platforms to provide suggestions that a user will most likely consume; thus, improving the user experience [1]. 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 [2]. 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: data and model scalability, model serving latency, model interpretability, and resource limitations, such as budget on compute and memory resources, 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 platform via which academic researchers become aware of the additional factors that may affect the chances of algorithm adoption into real production systems, and the performance of the algorithms if deployed. Industrial researchers will also benefit from sharing the practical frameworks at an industrial level.

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. With the reputation of KDD conference and the attendance of audiences across various domains, it could be expected that this workshop will bring up considerable attention.

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Regarding to COVID-19:

In light of the unfolding COVID-19 outbreak, we will be working closely with the KDD 2020 organization committee to investigate various feasible options towards a successful workshop.

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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:

1. Frameworks or end-to-end systems from industry are extremely welcomed.
2. Novel data mining and machine learning algorithms for scalable Recommender systems.
3. Personalization, including personalized product recommendation, streaming content recommendation, ads recommendation, news and article recommendation, etc.
4. New applications related to recommendation systems.
5. Existing or novel infrastructures for recommendation systems.
6. Approaches to handling practical challenges like feedback loops between recommendation algorithm outputs and how people respond to them.
7. Explainability of recommendations.
8. Fairness in recommender systems.
9. Recommendations under multi-objective and constraints.
10. Reproducibility of models and evaluation metrics.
11. Unbiased Recommendation

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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 (b)not double-blind(/b), and author names and affiliations should be listed. Submissions should includ 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/2020/

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