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Call for Papers
<div>Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS 2023)</div>
<div> https://kars-workshop.github.io/2023/</div>
<div> Sep. 18th - Sep. 22rd, 2023, Singapore</div>
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<div> Submission deadline: August 3rd, 2023, AoE</div>
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<div>[SCOPE]</div>
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<div>We are pleased to invite you to contribute to the Fifth Knowledge-aware and Conversational Recommender Systems Workshop held in conjunction with the ACM International Conference on Recommender Systems (RecSys 2023), Singapore, from September the 18th to
September the 22nd, 2023.</div>
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<div>In the last few years, a renewed interest of the research community in 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.</div>
<div>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 in offering recommendation capabilities by using the conversational paradigm.</div>
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<div>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.</div>
<div>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, describing the domain of interest of the recommendation
engine.</div>
<div>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.</div>
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<div>The Fifth 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), Deep Learning
(DL) – including Large Language Models (LLMs) –, Human-Computer Interaction (HCI), Information Retrieval (IR), 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.</div>
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<div>The aim of this fifth 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:</div>
<div>* share research and techniques, including new design technologies and evaluation methodologies;</div>
<div>* identify the next key challenges in the area;</div>
<div>* identify emerging topics in the field.</div>
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<div>[TOPICS]</div>
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<div>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.</div>
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<div>Topics of interest include, but are not limited to:</div>
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<div>- Knowledge-aware Recommender Systems.</div>
<div> - Models and Feature Engineering:</div>
<div> - Knowledge-aware data models based on structured knowledge sources (e.g., Linked Open Data, BabelNet, Wikidata, etc.)</div>
<div> - Semantics-aware approaches exploiting the analysis of textual sources (e.g., Wikipedia, Social Web, etc.)</div>
<div> - Knowledge-aware user modeling</div>
<div> - Methodological aspects (evaluation protocols, metrics, and data sets)</div>
<div> - Logic-based modeling of a recommendation process</div>
<div> - Knowledge Representation and Automated Reasoning for recommendation engines</div>
<div> - Deep learning methods to model semantic features</div>
<div> - Large language models (LLMs) for Knowledge-aware Recommender Systems</div>
<div> - Beyond-Accuracy Recommendation Quality:</div>
<div> - Using knowledge bases and knowledge graphs to increase recommendation quality(e.g., in terms of novelty, diversity, serendipity, or explainability)</div>
<div> - Explainable Recommender Systems</div>
<div> - Knowledge-aware explanations to recommendations (compliant with the General Data Protection Regulation)</div>
<div> - Online Studies:</div>
<div> - Using knowledge sources for cross-lingual recommendations</div>
<div> - Applications of knowledge-aware recommenders (e.g., music or news recommendation, off-mainstream application areas)</div>
<div> - User studies (e.g., on the user's perception of knowledge-based recommendations), field studies, in-depth experimental offline evaluations</div>
<div>- Conversational Recommender Systems.</div>
<div> - Design of a Conversational Agent:</div>
<div> - Design and implementation methodologies</div>
<div> - Dialogue management (end-to-end, dialog-state-tracker models)</div>
<div> - UX design</div>
<div> - Dialog protocols design</div>
<div> - Large language models (LLMs) for Conversational Recommender Systems</div>
<div> - User Modeling and interfaces:</div>
<div> - Critiquing and user feedback exploitation</div>
<div> - Short- and Long-term user profiling and modeling</div>
<div> - Preference elicitation</div>
<div> - Natural language-, multi-modal-, and voice-based interfaces</div>
<div> - Next-question problem</div>
<div> - Methodological and Theoretical aspects:</div>
<div> - Evaluation and metrics</div>
<div> - Datasets</div>
<div> - Theoretical aspects of conversational recommender systems</div>
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<div>[SUBMISSIONS]</div>
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<div>Submissions of full research papers must be in English, in PDF format in the CEUR-WS two-column conference format available at:</div>
<div>http://ceur-ws.org/Vol-XXX/CEURART.zip</div>
<div>or at:</div>
<div>https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/hpvjjzhjxzjk</div>
<div> if an Overleaf template is preferred.</div>
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<div>Submission will be peer-reviewed and accepted papers will appear in the CEUR workshop series. Papers may range from theoretical works to system descriptions.</div>
<div>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.</div>
<div>The conference language is English.</div>
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<div>We invite three kinds of submissions, which address novel issues in Knowledge-aware and Conversational Recommender Systems:</div>
<div>* 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.</div>
<div>* 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 in 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</div>
<div>* 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.</div>
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<div>The review process is single-blind. Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop.</div>
<div>Short and long paper submissions must be original work and may not be under submission to another venue at the time of review.</div>
<div>Accepted papers will appear in the workshop proceedings.</div>
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<div>Submission will be through Microsoft CMT at:</div>
<div>https://cmt3.research.microsoft.com/kars2023</div>
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<div>[IMPORTANT DATES]</div>
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<div>* Paper submissions due: August 3rd, 2023</div>
<div>* Paper acceptance notification: August 27th, 2023</div>
<div>* Camera-ready deadline: September 10th, 2023</div>
<div>* Workshop day: September 18th-22nd, 2023</div>
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Deadlines refer to 23:59 (11:59 pm) in the AoE (Anywhere on Earth) time zone.
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