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CALL FOR PAPER
Following the success of the several editions of MLRec in 2015, 2016, 2017, and 2018, the fifth 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.
* Topics of Interest
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.
-- Recommendation in Information Retrieval, ad industry, targeting ad, search ad, 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. Authors are required to submit their papers electronically in PDF format to the submission site by 11:59pm MDT, March 10, 2019. 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: March 10, 2019
Author Notification: Mar 25, 2019
Camera Ready Paper Due: Apr 15, 2019
Workshop: May 4, 2018
Xia Ning, Ohio State University
Deguang Kong, Yahoo Research
George Karypis, University of Minnesota
https://doogkong.github.io/2019/
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