posted by organizer: fsivrikaya || 2830 views || tracked by 6 users: [display]

MoST-Rec 2019 : Workshop on Model Selection and Parameter Tuning in Recommender Systems

FacebookTwitterLinkedInGoogle

Link: http://most-rec.gt-arc.com
 
When Nov 3, 2019 - Nov 7, 2019
Where Beijing, China
Submission Deadline Aug 14, 2019
Notification Due Aug 28, 2019
Final Version Due Jun 6, 2019
Categories    machine learning   recommender systems   model selection   artificial intelligence
 

Call For Papers

The MoST-Rec Workshop, co-located with CIKM 2019 (www.cikm2019.net/), is a place for discussing and exchanging recent advances and open challenges between the Model-Selection and Parameter Tuning community and the Recommendation Systems community.

Recommender systems (RS) have attracted strong attention of the machine learning community, especially within the last decades. Researchers have developed various algorithms proven to have good performance in the laboratory environment; however, applying them to real business cases is typically more difficult.

This workshop addresses the issues of algorithm selection and parameter tuning for recommender systems. The goal is to bring together researchers from the machine learning community with the industry representatives in order to exchange information on current challenges, constraints and ideas from both domains.

List of Topics:
We are looking for contributions that explain methods, challenges and insights at the intersection of Recommender Systems (RS), Model Selection (MS) and Parameter Tuning (PT) domains. In particular, topics of solicited papers include, but are not limited to:

* Model Selection and Parameter Tuning for Recommender Systems
- Ensemble methods
- Online model selection / ensembles
- Online boosting
- Parameter tuning
- High noise model selection / tuning
- Sparsely labeled model selection / tuning
- Distributing model selection or parameter tuning
* Recommender Systems applying model selection and parameter tuning methods
- Short term temporal dynamics (Item popularity, trends)
- Long term temporal dynamics (user tastes)
- Continously changing sets of users and items
- Scenarios with sparse rewards
- Tuning of robustness, convergence, lerning-rate
- Considerations of popularity bias (in the evaluation metrics, learning procedure)

*New*: Accepted papers will be considered for inclusion in a special issue of the Springer International Journal of Data Science and Analytics (JDSA).

Submission Guidelines:
All papers must be original and not simultaneously submitted to another journal or conference. Submissions should be made through the EasyChair Conference System (https://easychair.org/my/conference?conf=mostrec2019). Papers must be formatted according to ACM SIG proceedings format and prepared as a PDF file. Both regular papers and demo/poster papers are welcome:

- Long papers: Max 8 pages
- Short papers (including demo/poster): Max 4 pages

Contact:
All questions about submissions should be emailed to most-rec@gt-arc.com.

Related Resources

AAAI 2020   The Thirty-Fourth AAAI Conference on Artificial Intelligence
MoDRE 2019   9th International Workshop on Model-Driven Requirements Engineering (MoDRE)
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
RecSys 2019   13th ACM Conference on Recommender Systems
IWUAS 2020   2020 International Workshop on Unmanned Aircraft Systems (IWUAS 2020)
REC 2019   Special Session on Recommender Systems (ICCCI 2019 special session)
ECAI 2020   24th European Conference on Artificial Intelligence
RecNLP 2019   The AAAI 2019 Workshop on Recommender Systems and Natural Language Processing
ICMV--SPIE, Scopus, Ei Compendex 2019   SPIE--2019 The 12th International Conference on Machine Vision (ICMV 2019)--EI, Scopus and ISI
IS-AAD-MOPs@MCDM 2019   MCDM 2019: Invited Session on Automated Algorithm Design for Multi-objective Optimization Problems