-------- Forwarded Message -------- Subject: [AISWorld] [KaRS 2021] Third CfP: 3rd Knowledge-aware and Conversational Recommender Systems Workshop 2021 @ ACM RecSys Date: Fri, 18 Jun 2021 09:21:53 +0000 From: Vito Walter Anelli vitowalter.anelli@poliba.it To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org

Call for Papers Third Workshop on Knowledge-aware and Conversational Recommender Systems (KaRS 2021) https://kars-workshop.github.io/2021/ http://sisinflab.poliba.it/kars/2021/ Sep. 27th - Oct. 1st, 2021, Amsterdam, the Netherlands

Submission deadline: July 29th, 2021, 2021 AoE

[SCOPE]

We are pleased to invite you to contribute to the Third Workshop on Knowledge-aware and Conversational Recommender Systems held in conjunction with the ACM International Conference on Recommender Systems (RecSys 2021) Amsterdam, the Netherlands, from September the 27th to October the 1st, 2021.

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.

In addition, we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users' tastes and predict information that would probably be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating an explanation for the recommended items. Furthermore, this side information becomes crucial when a conversational interaction is implemented, in particular for the preference elicitation, explanation, and critiquing steps.

The 3rd Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop focuses on all aspects related to the exploitation of external and explicit knowledge sources to feed and build a recommendation engine, and on the adoption of interactions based on the conversational paradigm. The aim is to go beyond the traditional accuracy goal and to start a new generation of algorithms and approaches with the help of the methodological diversity embodied in fields such as Machine Learning (ML), Human-Computer Interaction (HCI), Information Retrieval (IR), and Information Systems (IS). Consequently, the focus lies on works improving the user experience and following goals such as user engagement and satisfaction or customer value.

The aim of this third edition of KaRS is to bring together researchers and practitioners around the topics of designing and evaluating novel approaches for recommender systems in order to: * share research and techniques, including new design technologies and evaluation methodologies; * identify next key challenges in the area; * identify emerging topics in the field.

[TOPICS]

This workshop aims at establishing an interdisciplinary community with a focus on the exploitation of (semi-)structured knowledge and conversational approaches for recommender systems and promoting collaboration opportunities between researchers and practitioners.

Topics of interests include, but are not limited to:

- Knowledge-aware Recommender Systems. - Models and Feature Engineering: - Knowledge-aware data models based on structured knowledge sources (e.g., Linked Open Data, BabelNet, Wikidata, etc.) - Semantics-aware approaches exploiting the analysis of textual sources (e.g., Wikipedia, Social Web, etc.) - Knowledge-aware user modeling - Methodological aspects (evaluation protocols, metrics, and data sets) - Logic-based modeling of a recommendation process - Knowledge Representation and Automated Reasoning for recommendation engines - Deep learning methods to model semantic features - Beyond-Accuracy Recommendation Quality: - Using knowledge-bases and knowledge-graphs to increase recommendation quality(e.g., in terms of novelty, diversity, serendipity, or explainability) - Explainable Recommender Systems - Knowledge-aware explanations to recommendations (compliant with the General Data Protection Regulation) - Online Studies: - Using knowledge sources for cross-lingual recommendations - Applications of knowledge-aware recommenders (e.g., music or news recommendation, off-mainstream application areas) - User studies (e.g., on the user's perception of knowledge-based recommendations), field studies, in-depth experimental offline evaluations - Conversational Recommender Systems. - Design of a Conversational Agent: - Design and implementation methodologies - Dialogue management (end-to-end, dialog-state-tracker models) - UX design - Dialog protocols design - User Modeling and interfaces: - Critiquing and user's feedback exploitation - Short- and Long-term user profiling and modeling - Preference elicitation - Natural language-, multi modal-, and voice-based interfaces - Next-question problem - Methodological and Theoretical aspects: - Evaluation and metrics - Datasets - Theoretical aspects of conversational recommender systems

[SUBMISSIONS]

Submissions of full research papers must be in English, in PDF format in the CEUR-WS two-column conference format available at: http://ceur-ws.org/Vol-XXX/CEURART.zip or at: https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-wo... if an Overleaf template is preferred.

Submission will be peer-reviewed and accepted papers will appear in the CEUR workshop series. Papers may range from theoretical works to system descriptions. We particularly encourage Ph.D. students or Early-Stage Researchers to submit their research. We also welcome contributions from the industry and papers describing ongoing funded projects which may result useful to the Knowledge-aware and Conversational Recommender Systems community. The conference language is English.

We invite three kinds of submissions, which address novel issues in Knowledge-aware and Conversational Recommender Systems: * Long Papers should report on substantial contributions of lasting value. The Long papers must have a length of a minimum of 6 and a maximum of 8 pages (plus an unlimited number of pages for references). Each accepted long paper will be included in the CEUR online Workshop proceedings and presented in a plenary session as part of the Workshop program. * Short/Demo Papers typically discuss exciting new work that is not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance to this category despite not having gone through sufficient experimental validation or lacking a strong theoretical foundation. Applications of recommender systems to novel areas are especially welcome. The Short/Demo papers must have a length of a minimum of 3 and a maximum of 5 pages (plus an unlimited number of pages for references). Each accepted short paper will be included in the CEUR online Workshop proceedings * Position/Discussion Papers describe novel and innovative ideas. Position papers may also comprise an analysis of currently unsolved problems, or review these problems from a new perspective, in order to contribute to a better understanding of these problems in the research community. We expect that such papers will guide future research by highlighting critical assumptions, motivating the difficulty of a certain problem, or explaining why current techniques are not sufficient, possibly corroborated by quantitative and qualitative arguments. The Position/Discussion papers must have a length of a minimum of 2 and a maximum of 3 pages (plus an unlimited number of pages for references). Original Position/Discussion accepted papers will be included in the CEUR online Workshop proceedings. Selected Position/Discussion papers will be invited as oral presentations.

The review process is single-blind. Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Moreover, following the RecSys 2021 guidelines, reviewers will be asked to comment on whether the length is appropriate for the contribution. Shorter papers should generally report on advances that can be described, set into context, and evaluated concisely. Longer papers should reflect substantial contributions of lasting value. Short and long paper submissions must be original work and may not be under submission to another venue at the time of review. Accepted papers will appear in the workshop proceedings.

Submission will be through Easychair at:

https://easychair.org/conferences/?conf=kars2021

[IMPORTANT DATES]

* Paper submissions due: July 29th, 2021 * Paper acceptance notification: August 21st, 2021 * Camera-ready deadline: August 28th, 2021 * Workshop day: Sep 27th - Oct 1st, 2021

Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. Informativa Privacy - Ai sensi del Regolamento (UE) 2016/679 si precisa che le informazioni contenute in questo messaggio sono riservate e ad uso esclusivo del destinatario. Qualora il messaggio in parola Le fosse pervenuto per errore, La preghiamo di eliminarlo senza copiarlo e di non inoltrarlo a terzi, dandocene gentilmente comunicazione. Grazie. Privacy Information - This message, for the Regulation (UE) 2016/679, may contain confidential and/or privileged information. If you are not the addressee or authorized to receive this for the addressee, you must not use, copy, disclose or take any action based on this message or any information herein. If you have received this message in error, please advise the sender immediately by reply e-mail and delete this message. Thank you for your cooperation. _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org