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 at the intersection between RecSys and NLP. Areas of interest include, but are not limited to:

1.      Applications that inherently combine RecSys and NLP. E.g., using textual reviews for improving recommendations

2.      Using NLP techniques for RecSys. E.g., considering recommendations as a language modeling problem.

3.      Using RecSys techniques for NLP. E.g., personalization of sentiment analysis.

RecNLP is a venue for discussion, and as such we allow submission of manuscripts that have already been published or are currently under review, as well as original submissions. The ideal length of a paper is between 4-8 pages, but there are no strict page limits. Already-published papers should be accompanied by a short abstract justifying their specific relevance to RecNLP.

Manuscripts must be submitted through easychair and will be reviewed by a program committee. The review process is single-blind. That is, authors’ names should not be anonymized.

WORKSHOP FORMAT

The workshop is composed of invited talks and oral talks of refereed papers, where we allow time for fruitful discussion in the end of each talk. Workshop duration is half a day.

ORGANIZATION

Oren Sar Shalom, Intuit, oren_sarshalom@intuit.com

Vahid Noroozi, University of Illinois at Chicago, vnoroo2@uic.edu

Mengting Wan, UC San Diego, m5wan@ucsd.edu

Julian McAuley, UC San Diego, jmcauley@ucsd.edu

For more details: https://recnlp2019.github.io

Conference submission page: https://easychair.org/conferences/?conf=recnlp2019