EVENT REPORT

Report on the International Workshop on
Natural Language Processing for
Recommendations (NLP4REC 2020)
Workshop held at WSDM 2020
Xiao Huang
The Hong Kong Polytechnic University
xhuang.polyu@gmail.com
Zhaochun Ren
Shandong University
zhaochun.ren@sdu.edu.cn

Pengjie Ren
University of Amsterdam
p.ren@uva.nl

Fei SunAlibabaofey.sunfei@gmail.com

Xiangnan He
University of Science and Technology of China
xiangnanhe@gmail.com

Dawei Yin
JD.comyindawei@acm.org

Maarten de Rijke
University of Amsterdam & Ahold Delhaize
derijke@uva.nl
Abstract
This paper summarizes the outcomes of the International Workshop on Natural Language
Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7,2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques
to tackle the challenges in constructing more intelligent recommender systems. Specific topics
included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.

1Introduction

NLP4REC 2020 was a half-day workshop held at the 13th International Conference on Web Searchand Data Mining (WSDM 2020). A major goal of the workshop was to stimulate discussions on
the challenges and opportunities in employing natural language processing (NLP) techniques toaddress problems in recommender systems, such as lack of explainability and reproducibility, cold
start issues, and privacy issues.
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NLP4REC 2020 was held at WSDM 2020. WSDM (pronounced “wisdom”) is a conference
that is focused on both academic and industrial research in the areas of search and data mining.
It is one of the premier conferences for researchers who work on information retrieval. WSDM
2020 was held in Houston, Texas, from February 3–7, 2020.
The recommendation task, one of the major topics in WSDM, aims to retrieve the relevant
information based on user-item interaction behaviors. Though it has been studied for decades, offthe-shelf recommendation frameworks are still far from meeting customer satisfaction in complex
practical systems. As rich user-generated data is involved, recently, many efforts have been devoted
to leveraging NLP technologies to improve the performance of recommender systems.
NLP4REC 2020 provided the opportunity for researchers and practitioners who are interested
in exploring NLP for recommendations, to learn and communicate. Nineteen participants fromdifferent organizations and institutions attended the workshop. The specific topics discussed
during the workshop include connections between NLP and recommender systems, knowledgeaware recommendation, explainable recommendation, conversational recommendation, sequentialrecommendation, reproducibility of recommendation frameworks, and future directions.

2Workshop Theme and Topics

Previous similar workshops have focused on either recommender systems only or interdisciplinary
research with a particular task of natural language processing. Examples include similar workshops that have been held on RecSys, SIGIR and WSDM, i.e., KaRS,1 RECOVER,2 EARS,3 and
CARS-BDA.4 NLP4REC 2020 was a forum for discussions about the challenges in applying NLP
technologies to real recommendation applications as well as the theories behind them.
The main theme was to establish a bridge for communication between industrial researchers
and academic researchers, and provide an opportunity for people to exchange ideas, and discuss
future directions.
The topics of interest were focused on the applications of NLP technologies in the following
recommendation scenarios.
Knowledge-aware recommendation. The knowledge-aware recommendation has beenshown to be an effective way to alleviate the cold start issue, which is a common one in mostrecommender systems due to the sparsity of user-item interactions. The idea is to incorporate freetext or structured knowledge, e.g., user profiles, item profiles, knowledge graph, so as to minimize
dependence on interaction data. In particular, recent studies that apply deep learning architectures on graph-structured data have achieved remarkable performance in many tasks, including
recommender systems [Zhu et al., 2019, Chen et al., 2019].
Explainable recommendation. The explainable recommendation aims to improve transparency and persuasiveness of recommender systems by providing explanations to users or developers, which helps them to understand why certain items are recommended [Zhang and Chen,2018]. Much progress has been made to improve recommendation explainability by paying atten1

https://recsys.acm.org/recsys18/kars/
https://recsys.acm.org/recsys18/recover/3
http://www.sigir.org/sigir2019/program/workshops/ears/
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http://wise-conferences.org/CARS-BDA/CARS-BDA.html
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tion to certain user/item attributes [Yang et al., 2019]. Recently, there have been some studies
on generating natural language explanations [Lin et al., 2019].
Conversational recommendation. The conversational recommendation introduces thecombination of both conversation and recommendation modeling. Instead of relying merely on previous user-item interaction behaviors to capture user interests precisely, conversational recommendation provides an alternative strategy by conducting conversations with the users directly [Sunand Zhang, 2018].
Sequential recommendation. The sequential recommendation is an effective paradigm tocapture the dynamics of recommender systems by modeling user-item interactions as a sequence.
Since it shares the sequential characteristics with natural languages, many NLP technologies ormechanisms have been successfully applied to model various scenarios in sequential recommendations [Ren et al., 2019, Ma et al., 2019, Sun et al., 2019].

3Workshop Contributions

Focusing on the theme of incorporating NLP techniques into recommendations, NLP4REC consisted of four parts, including two keynotes, an invited talk, and a hands-on panel.

3.1

Keynotes

Conversation in Recommendation, Opportunities and Challenges – Jian-Yun Nie,
University of Montreal, Canada
Most current approaches to recommendation operate as a black box without explicit userinteractions. A new form of interactive recommendation – conversational recommendation – isemerging in both industry and academia. To perform recommendations, the system needs tointeract with the user, through conversation, and determine the need of the user and make goodchoices. In this talk, Dr. Nie discussed what a conversational recommender can do beyond traditional systems, the possible approaches to be used, as well as the challenges.
Personalized Language Modeling, from Prediction to Generation and Justification –Julian McAuley, University of California San Diego, USADr. McAuley gave a historical perspective on the use of NLP for recommendation, with aparticular focus on personalized language generation. Examples included models for question
answering, text and recipe generation, and interpretability. By personalizing these models, we are
better able to adapt to both the preferences and the linguistic nuances of individuals.

3.2

Invited talkLearning From Users Interactions – Julia Kiseleva, Microsoft Research AI, USA
Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. In general, suchobjectives could be manually crafted and rarely capture complex user needs accurately. Dr. Kiseleva discussed how to infer objectives directly from observed user interactions. Such inferences
can be made regardless of prior knowledge and across different types of user behavior.
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3.3

Panel on natural language processing for recommender systems

Panel Moderator:• Xiao Huang, The Hong Kong Polytechnic University, China.
Panelists:• Jian-Yun Nie, University of Montreal, Canada.
• Julian McAuley, University of California San Diego, USA.
• Julia Kiseleva, Microsoft Research AI, USA.
Following the three talks, the panel of NLP4REC involved intensive interactions between theaudience and the three speakers. Several topics around NLP for recommendations were discussed,including the development, the challenges, the state-of-the-art progress, and the future directions.
Three major questions as follows were explored.
• Q1: Are we really making much progress with recent neural recommendation
approaches? The reproducibility of state-of-the-art deep recommendation frameworks has
been discussed. We arrived at a consensus that open source should be encouraged.
• Q2: What progress has been made towards NLP4REC? Many techniques in NLP
have been employed in recommender systems. We discussed several ones, including machinetranslation, dialogue and interactive systems, generation, question answering, sentiment
analysis, and argument mining.
• Q3: What are the future directions and challenges on NLP4REC? Many promising
recommendation problems are attracting increasing attention, including explainable recommendation, conversational recommendation, personalized recommendation, sequential recommendation, and knowledge-aware recommendation.

4Conclusion

The international workshop on natural language processing for recommendations has brought
together researchers from both NLP and recommendation perspectives. It has explored four important research topics: knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation. The workshop was divided into fourparts, including two keynotes, an invited talk, and an interactive panel discussion. Three seniorresearchers were invited to present their research on conversational recommendation, personalizedlanguage modeling, and learning from user interactions, respectively. Following the three talks,a panel discussion was organized around several NLP for recommendation topics. We hope thatthe lectures and panel discussions at the workshop to inspire future research on this field. We
also plan to continue the discussions about NLP4REC at venues like WSDM 2021 or The Web
Conference 2021.

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Acknowledgements
We want to thank all the people who encouraged us and helped us to make the NLP4RECworkshop happen. In particular, we would like to express our gratitude to our keynote andinvited talk speakers: Jian-Yun Nie, Julian McAuley, and Julia Kiseleva. We would also like tothank all the people who participated in the workshop, for useful discussions and opportunities
to exchange ideas, and to imagine future research problems on natural language processing forrecommendations.

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