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DLRS 2017 - 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017)



VenueComo, Italy Italy



Topics/Call fo Papers

2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017). The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to promote research in Deep Learning methods specific to Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities. The workshop is held in conjunction with the 11th ACM Conference on Recommender Systems (RecSys 2017), the premier venue for Recommender Systems research. The workshop will be held on one of the workshop days of the conference (the 27th or the 31st of August 2017) in Como, Italy.
We encourage theoretical, experimental, and methodological developments advancing state-of-the-art knowledge in the area of Recommender Systems and Deep Learning. Areas of interest also encompass novel applications, using Deep Learning to solve the still-standing challenges in personalization technology, and applications of Deep Learning in related fields with clear relation to Recommender Systems. Topics include, but are not limited to the following:
I. User and item representations
• Enhancement of existing recommendation algorithms through deep learning methods
• Learning representations of items and/or users using multiple information sources
II. Dynamic behavior modeling
• Dynamic temporal user behavior modeling
• Session and intention modeling
III. Specialized recommendation methods using deep learning techniques
• Incorporating unstructured data sources such as text, audio, video or image into recommendation algorithms
• Context-aware recommender systems
• Handling the cold-start problem with deep learning
• Application specific deep learning based recommenders (e.g. music recommenders)
IV. Architecture
• Novel deep neural network architectures for a particular recommendation task
• Scalability of deep learning methods for real-time applications
• Advances in deep learning technology for large scale recommendation
• Special layers or units designed for recommender systems
• Special activation functions or operators designed for recommender systems
V. Novel evaluation and explanation techniques
• Evaluation and comparison of deep learning implementations for a recommendation task
• Modeling the state of the user
• Sensitivity analysis of the network architecture
• Explanation of recommendations based on deep learning
Submissions and reviews are handled electronically via EasyChair at the following address: Submissions should be prepared in PDF format according to the standard double-column ACM SIG proceedings format. Authors must submit their papers to simultaneously and send the assigned arxiv ID to the organizers via email when it is available. Failing to send the arxiv ID within at most two weeks from the submission deadline will result in the rejection of the paper.
The ideal length of a paper for DLRS 2017 is between 4-8 pages, but submissions have no strict page limits. Although the authors should avoid submitting unnecessarily long papers in order not to overwhelm reviewers.
DLRS 2017 accepts original and novel contributions that are neither published nor under review in other venues. Self publishing of the submitted papers in public repositories is permitted and encouraged. We also encourage authors to make their code and datasets publicly available.
Papers must be electronically submitted through EasyChair by 23:59 (AoE timezone) on the 22nd of June, 2017. The authors must also submit their papers to simultaneously and email the arxiv ID to the organizers on the workshop’s email address.
All papers are peer reviewed by at least 3 members of the Program Committee consisting of researchers of deep learning and recommender systems.
Accepted papers are published in the workshop proceedings and indexed in the ACM Digital Library. Accepted papers are given either an oral or a poster presentation slot at the workshop. At least one author of every accepted paper must attend the workshop and present their work.

Last modified: 2017-04-11 23:10:29