MLRec 2018 CFP

[ML Rec]

Dear Researchers,



Please accept our apologies if you receive multiple copies or you are not
interested in it.



*https://easychair.org/cfp/MLRec2018 <https://easychair.org/cfp/MLRec2018>*



MLRec 2018: 4th International Workshop on Machine Learning Methods for
Recommender Systems

San Diego Marriott Mission Valley, 8757 Rio San Diego Drive, San Diego,
California 92108

San Diego, CA, United States, May 2-5, 2018

•MLRec 2018 <https://easychair.org/cfp/MLRec2018#CFP:1>•Submission
Guidelines <https://easychair.org/cfp/MLRec2018#CFP:2>•Organizing committee
<https://easychair.org/cfp/MLRec2018#CFP:3>•Venue
<https://easychair.org/cfp/MLRec2018#CFP:4>•Contact
<https://easychair.org/cfp/MLRec2018#CFP:5>



*Conference website*

https://doogkong.github.io/2018/index.html

*Submission link*

https://easychair.org/conferences/?conf=mlrec2018

*Submission deadline*

December 23, 2017



*Topics: recommender system
<https://easychair.org/cfp/topic.cgi?a=16781028;tid=5207> recommender
algorithm
<https://easychair.org/cfp/topic.cgi?a=16781028;tid=444877> empirical study
of recommender system
<https://easychair.org/cfp/topic.cgi?a=16781028;tid=19385147> deep learning
for recommendation
<https://easychair.org/cfp/topic.cgi?tid=17697352;a=16781028>*

*MLRec 2018*

4th International Workshop on Machine Learning Methods for Recommender
Systems

In conjunction with 18th SIAM International Conference on Data Mining (SDM
2018) <http://www.siam.org/meetings/sdm18/>
May 3 - 5, 2018, San Diego, CA, USA

Following the success of the several editions of MLRec in 2015
<https://doogkong.github.io/2015/>, 2016 <https://doogkong.github.io/2016/>
, 2017 <https://doogkong.github.io/2017/>, the fourth edition of the MLRec
workshop focuses on developing novel, and applying existing Machine
Learning (ML) and Data Mining (DM) methods to improve recommender systems.
This workshop also highly encourages applying ML-based recommendation
algorithms in novel application domains (e.g., precision medicine), deep
learning for recommendation, and solving novel recommendation problems
formulated from industry. The ultimate goal of the MLRec workshop series is
to promote the advancement and implementation of new, effective and
efficient ML and DM techniques with high translational potential for real
and large-scale recommender systems, and to expand the territory of
ML-based recommender system research toward non-conventional application
areas where recommendation problems largely exist but haven't been fully
recognized.

*Submission Guidelines*

All papers must be original and not simultaneously submitted to another
journal or conference. We encourage submissions on a variety of topics,
including but not limited to:

   - Novel machine learning algorithms for recommender systems, e.g., new
   content-based or context-aware recommendation algorithms, new algorithms
   for matrix factorization, tensor-based approaches for recommender systems,
   etc.
   - Novel applications of existing machine learning and data mining
   algorithms for recommender systems, e.g., applying bilinear models,
   (non-convex) sparse learning, metric learning, low-rank
   approximation/PCA/SVD, neural networks and deep learning, etc.
   - Novel optimization techniques for improving recommender systems, e.g.,
   parallel/distributed optimization techniques, efficient stochastic gradient
   descent, etc.
   - Industrial practices and implementations of recommendation systems,
   e.g., feature engineering, model ensemble, large-scale implementations of
   recommender systems, etc.
   - Emerging recommendation problems and scenarios in industry and their
   ML-based solutions, e.g., recommendation for e-fashion, etc.
   - Novel recommendation problems in non-conventional recommender system
   research areas (e.g., precision medicine, health informatics) and their
   ML-based solutions, e.g., recommendation of physicians, recommendation of
   healthy life-styles for seniors, etc.
   - Enhanced deep learning methods for recommender systems, e.g., word
   embedding techniques, CNN, RNN and LSTM, Generative Advertiseral Networks
   (GAN), auto-encoder, RBM, etc

Submission Instructions

The workshop accepts long paper and short (demo/poster) papers. Short
papers submitted to this workshop should be limited to 4 pages while long
papers should be limited to 8 pages. All papers should be formatted using
the SIAM SODA macro <http://www.siam.org/proceedings/macros.php>. Authors
are required to submit their papers electronically in PDF format to the
submission site <https://easychair.org/conferences/?conf=mlrec2018> by
11:59pm MDT, *Dec 23, *2017. The site has started to accept manuscripts. At
least one author of each accepted paper should be registered to the
conference.

Important Dates

   - Paper Submission Deadline: December 23 2017
   - Author Notification: January 23, 2018
   - Camera Ready Paper Due: February 1, 2018
   - Workshop: May 5, 2018

*Organizing committee*

   - *Deguang Kong* <https://sites.google.com/site/doogkong/>, Yahoo
   Research
   - *Xia Ning <http://cs.iupui.edu/~xning/>, *Indiana University – Purdue
   University Indianapolis
   - *George Karypis <http://glaros.dtc.umn.edu/gkhome/index.php>, *University
   of Minnesota

*Venue*

The conference will be held in In conjunction with 18th SIAM International
Conference on Data Mining (SDM 2018) <http://www.siam.org/meetings/sdm18/>
May 3 - 5, 2018, San Diego, CA, USA

*Contact*

All questions about submissions should be emailed to doogkong at gmail.com,
xning at iupui.edu.



Regards,

Organizers
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