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.

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