The AAAI-19 Workshop on Recommender Systems and Natural Language Processing (RecNLP)
Held at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI'19)
Jan 27-28, 2019 - Honolulu, Hawaii, USA

Introduction

RecNLP is an interdisciplinary workshop covering the intersection between Recommender Systems (RecSys) and Natural Language Processing (NLP). The primary goal of RecNLP is to identify common ideas and techniques that are being developed in both disciplines, and to further explore the synergy between the two and to bring together researchers from both domains to encourage and facilitate future collaborations.

Call for Papers

We encourage theoretical, experimental, and methodological developments advancing state-of-the-art knowledge in the intersection between RecSys and NLP. Areas of interest include, but not limited to:

  • Applications that inherently combine RecSys and NLP. E.g., using textual reviews for improving recommendations
  • Using NLP techniques for RecSys. E.g., considering recommendations as a language modeling problem.
  • Using RecSys techniques for NLP. E.g., personalization of sentiment analysis.

RecNLP is a venue for discussion, and no official proceedings will be published. We allow submission of manuscripts that have already been published or are currently under review, as well as original ones. The ideal length of a paper is between 4-8 pages, but there is no strict page limits. Note that as there are no formal proceedings to RecNLP, submissions are not taken into account with respect to publication in other venues. Already-published papers should be accompanied by a cover abstract justifying their contribution specifically to RecNLP.
Manuscripts must be submitted through an online submission system (EasyChair) and will be reviewed by a program committee. The review process is a single-blind. That is, authors’ names should not be anonymized.
You can submit your manuscript from the following link: https://easychair.org/cfp/recnlp2019
Any questions may be directed to the workshop e-mail address: oren_sarshalom@intuit.com.

Key Dates

 

Paper Submission Deadline: Nov 5th, 2018 - 11:59PM Alofi Time

Author Notification: Nov 26th, 2018

Camera Ready Version: TBA

Workshop: Jan 27-28, 2019

Schedule

TBA
Jan 27-28, 2019

 

Speaker

Philip S. Yu

Philip S. Yu

UIC Distinguished Professor and
Wexler Chair in Information Technology
University of Illinois at Chicago

Broad Learning via Fusion of Heterogeneous Information for Recommendations


Bio
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining.
Dr. Yu has published more than 1,100 referred conference and journal papers cited more than 100,000 times with an H-index of 148. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).

Accepted Papers

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PC Members

 
 

Yi Tay (Nanyang Technological University)

Jianmo Ni (University of California, San Diego)

Philip S. Yu (University of Illinois at Chicago)

Lei Zheng (University of Illinois at Chicago)

Rose Catherine Kanjirathinkal (Carnegie Mellon University)

 

Workshop Organizers

 
 

Vahid Noroozi

University of Illinois at Chicago

 

Mengting Wan

University of California, San Diego

 

Julian McAuley

University of California, San Diego