posted by user: balazshidasi || 2259 views || tracked by 8 users: [display]

DLRS 2018 : 3rd Workshop on Deep Learning for Recommender Systems

FacebookTwitterLinkedInGoogle

Link: http://dlrs-workshop.org/dlrs-2018/call-for-papers/
 
When Oct 6, 2018 - Oct 6, 2018
Where Vancouver
Submission Deadline Jul 16, 2018
Notification Due Aug 13, 2018
Final Version Due Aug 27, 2018
Categories    recommender systems   deep learning   machine learning   information retrieval
 

Call For Papers

We would like to invite you to participate in the 3rd Workshop on Deep Learning for Recommender Systems (DLRS 2018). The aim of the workshop is to further 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 12th ACM Conference on Recommender Systems (RecSys 2018), the premier venue for Recommender Systems research. The workshop will be held on the 6th of October 2018 in Vancouver, Canada.


TOPICS OF INTEREST
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. This year we further encourage exploring new research directions and application domains. Topics include, but are not limited to the following:

I. Generative models for recommendations
Recommending sets of items
Diversification of recommendations
Data augmentation

II. Novel domains and uses of recommendations made possible by deep learning
Novel domains
Privacy-aware recommendations

III. User and item representations
Enhancement of existing recommendation algorithms through deep learning methods
Learning representations of items and/or users using multiple information sources

IV. Dynamic behavior modeling
Dynamic temporal user behavior modeling
Session and intention modeling

V. 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)

VI. 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

VII. 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


PAPER FORMAT AND SUBMISSION
Submissions and reviews are handled electronically via EasyChair at the following address: https://easychair.org/conferences/?conf=dlrs2018. Submissions should be prepared in PDF format according to the standard double-column ACM SIG proceedings format. Authors must submit their papers to arxiv.org simultaneously and send the assigned arxiv ID to workshop email address once it is assigned. 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 2018 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 2018 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 16 July, 2018. The authors must also submit their papers to arxiv.org 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 (published in ACM ICPS) 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.

Related Resources

RecSys 2019   13th ACM Conference on Recommender Systems
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
UMUAI FatRec 2019   UMUAI Special issue on: Fair, Accountable, and Transparent Recommender Systems
CAIP 2019   Computer Analysis of Images and Patterns
VBS 2020   Video Browser Showdown
BIOA 2019   Bio-inspired algorithms and Bio-systems (Mathematical Biosciences and Engineering)
ACMLC--JA, Scopus 2019   2019 3rd Asia Conference on Machine Learning and Computing (ACMLC 2019)--JA, Scopus
ECML PKDD 2019   Joint Call for Papers for the Research and ADS tracks, ECML PKDD 2019
LOD 2019   5th International Conference on machine Learning, Optimization & Data science