Конференции

2nd International Workshop on Industrial Recommendation Systems

Технические науки
Сингапур

14 августа–18 августа 2021

Форма участия: Дистанционная

Срок подачи заявок: 20.05.2021

Организаторы: Organizing Committees

emal: [email protected] or [email protected] for any questions

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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 with user feedback loop
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.

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