Introduction
The workshop centers around the use of Deep Learning technology in Recommender Systems and algorithms. DLRS 2017 builds upon the positively received traits of DLRS 2016. DLRS 2017 is a fast paced half-day workshop with a focus on high quality paper presentations and keynote. We welcome original research using deep learning technology for solving recommender systems related problems. Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the recommender systems community, deep learning for recommender systems became widely popular in 2016. We believe that the previous edition of this workshop—DLRS 2016—also took its share to popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities.
Call for paper
Important date
Submission Topics
I. User and item representations
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Enhancement of existing recommendation algorithms through deep learning methods
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Learning representations of items and/or users using multiple information sources
II. Dynamic behavior modeling
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Dynamic temporal user behavior modeling
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Session and intention modeling
III. Specialized recommendation methods using deep learning techniques
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Incorporating unstructured data sources such as text, audio, video or image into recommendation algorithms
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Context-aware recommender systems
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Handling the cold-start problem with deep learning
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Application specific deep learning based recommenders (e.g. music recommenders)
IV. Architecture
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Novel deep neural network architectures for a particular recommendation task
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Scalability of deep learning methods for real-time applications
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Advances in deep learning technology for large scale recommendation
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Special layers or units designed for recommender systems
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Special activation functions or operators designed for recommender systems
V. Novel evaluation and explanation techniques
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Evaluation and comparison of deep learning implementations for a recommendation task
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Modeling the state of the user
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Sensitivity analysis of the network architecture
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Explanation of recommendations based on deep learning
All Comments
Important Date
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Aug 27
2017
Conference Date
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Jun 22 2017
Draft paper submission deadline
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Jul 14 2017
Draft Paper Acceptance Notification
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Aug 27 2017
Registration deadline
Sponsored By
Contact Information
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