CFP Extended: 5th ACM RecSys'17 Workshop on Large Scale Recommender Systems (LSRS2017)

[Denis Parra]

  ** Due to several requests the deadline has been extended ** 

================ CFP LSRS 2017 ============ 

5th Workshop on Large Scale Recommender Systems

co-located at ACM RecSys 2017

Lake Como, Italy

https://lsrs2017.wordpress.com

This workshop aims to foster discussions in several fields that are of interest to our growing community of recommendation system builders. On the practical side, we would like to encourage sharing of architecture and algorithm best practices in large-scale recommender systems as they are practiced in industry, as well as particular challenges and pain points. We hope this will guide future research that is system aware. On the research side, we focus on bringing in ideas and evaluations on scaling beyond the current generation of big data systems, with improved recommendation metrics. We believe the brightest minds from both sides will mutually benefit from the discussions and accelerate problem solving.

Submission formats:

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We invite submissions in two formats: extended abstracts (1-8 pages), or slides (15-20 slides). 

We encourage contributions in new theoretical research, practical solutions to particular aspects of scaling a recommender, best practices in scaling evaluation systems, and creative new applications of big data to large scale recommendation system.

Important Dates:

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Submission: June 25, 2017

Notification: July 15, 2017

Camera-ready version: August 18, 2017

Topics:

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Our topics of interests include, but are not limited to: 

Data & Algorithms in Large-scale RS:

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Scalable deep learning algorithm

Big data processing in offline/near-line/online modules

Data platforms for recommendation

Large, unstructured and social data for recommendation

Heterogeneous data fusion

Sampling techniques

Parallel algorithms

Algorithm validation and correctness checking

Systems of Large-scale RS:

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Architecture

Programming Model

Cloud platforms best for recommenders

Real-time recommendation

Online learning for recommendation

Scalability and Robustness

Evaluation of Large-scale RS:

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Comparison of algorithms’ application and effectiveness in different domains

Offline optimization and online measurement consistency

Evaluation metrics alignment with product/project goal

Large user studies

A/B testing methodology

Organizers:

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Tao Ye (Pandora Inc)

Denis Parra (PUC Chile)

Vito Ostuni (Pandora Inc)

Tao Wang (Apple Inc)