[Topic-models] CFP : ACM RecSys'17 Workshop on Large Scale Recommender Systems (LSRS2017)

Denis Parra denisparra at gmail.com
Thu Mar 23 07:36:38 EDT 2017


apologies for cross-postings

================ 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:
===============
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:
============
Submission: June 22, 2017
Notification: July 29, 2017
Camera-ready version: August 18, 2017

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

Data & Algorithms in Large-scale RS:
====================================
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:
==========================
Architecture
Programming Model
Cloud platforms best for recommenders
Real-time recommendation
Online learning for recommendation
Scalability and Robustness

Evaluation of Large-scale RS:
==========================
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:
=========
Tao Ye (Pandora Inc)
Denis Parra (PUC Chile)
Vito Ostuni (Pandora Inc)
Tao Wang (Apple Inc)
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