[RecSys 2024 - KaRS DEADLINE EXTENDED] 4th CfP: 6th Knowledge-aware and Conversational Recommender Systems WS 2024 @ ACM RecSys
Vito Walter Anelli <vitowalter.anelli-ZrEezxGRMK/[email protected]> Thu, 29 Aug 2024 15:33:33 +0000
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4th Call for Papers
Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS=
2024)
https://kars-workshop.github.io/2024/
Oct. 14th - Oct. 18th, 2024, Bari
DEADLINE EXTENDED - Submission deadline: September 6=
th, 2024, AoE
We are pleased to invite you to contribute to the Sixth Knowledge-aware and=
Conversational Recommender Systems Workshop held in conjunction with the A=
CM International Conference on Recommender Systems (RecSys 2024), Bari, Ita=
ly, from October the 14th to October the 18th, 2024.
# Scope
Recommender systems have achieved ubiquity across various domains, ranging =
from e-commerce to media content suggestions, playing pivotal roles in faci=
litating user online experiences. However, despite their prevalence, these =
systems often encounter challenges in effectively engaging with human users=
. While data-driven algorithms have demonstrated success in uncovering late=
nt connections within user-item interactions, they frequently overlook the =
central actor in this loop: the end-user.
A prevalent behavior among human users, which is rarely encoded in recommen=
dation engines, is the utilization of domain-specific knowledge. Fortunatel=
y, Knowledge-aware Recommender Systems are garnering increasing attention w=
ithin the recommendation community. By leveraging explicit domain knowledge=
represented via ontologies or knowledge graphs, these approaches can under=
stand the semantic relationships between users, items, and other entities, =
thus offering tailored recommendations to users and addressing inherent lim=
itations of purely data-driven systems. Despite their existence for over tw=
o decades, their significance has been revitalized due to the Linked Open D=
ata initiative and the availability of large knowledge-graphs such as DBped=
ia and Wikidata.
Linked data and their ontologies underpin many recommendation approaches an=
d challenges proposed in recent years, such as Knowledge Graph embeddings, =
hybrid recommendation, link prediction, knowledge transfer, interpretable r=
ecommendation, and user modeling.
Moreover, a new wave in this domain is marked by the emergence of neuro-sym=
bolic systems, integrating data-driven methodologies with symbolic reasonin=
g. This fusion of machine learning systems, adept at harnessing data, with =
symbolic approaches, adept at leveraging knowledge, holds promise in enhanc=
ing recommendation quality, particularly in scenarios with sparse training =
data.
In parallel, the rise of Conversational Recommender Systems (CRSs) highligh=
ts the crucial role of content features in facilitating user interactions, =
particularly in multi-turn dialogues between users and the system, which br=
ing about novel challenges, such as the incorporation of both short- and lo=
ng-term preferences, prompt adaptation to user feedback, the limited availa=
bility of datasets, and the evaluation beyond simple accuracy metrics.
In this context, the emergence of Large Language Models (LLMs) is pivotal a=
nd has breathed new life into CRSs. LLMs significantly impact CRSs by lever=
aging their advanced capabilities in understanding user queries and generat=
ing relevant recommendations in natural language. They excel in processing =
complex and nuanced user inputs, allowing for more seamless and engaging in=
teractions between users and the system. Moreover, LLMs contribute to the a=
daptability and responsiveness of CRSs by continuously learning from user i=
nteractions, thus refining their recommendations and improving the user exp=
erience over time.
The **Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) W=
orkshop** aims to spark a new generation of research that prioritizes user =
experience, engagement, and satisfaction over mere accuracy.
Drawing upon a multifaceted set of expertise including **Machine Learning**=
, **Deep Learning**, **Human-Computer Interaction**, **Information Retrieva=
l**, and **Information Systems**, this workshop endeavors to catalyze a fre=
sh wave of research.
KaRS serves as a dynamic forum where researchers and practitioners, scholar=
s and industry professionals, converge to not only disseminate their latest=
findings but also to identify the emerging topics, delineate the next key =
challenges, and forecast the future opportunities for research and developm=
ent.
Through fostering active participation and facilitating the exchange of ide=
as, KaRS aims to cultivate an interdisciplinary community that collaborates=
on the topics of knowledge-aware and conversational recommenders, alongsid=
e the emerging topics of LLMs and neuro-symbolic methodologies, which furth=
er extend the scope of this new edition of KaRS.
# Topics
This workshop aims at establishing an interdisciplinary community with a fo=
cus on the exploitation of (semi-)structured knowledge and conversational a=
pproaches for recommender systems and promoting collaboration opportunities=
between researchers and practitioners.
Topics of interest 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 e=
ngines
- Deep learning methods to model semantic features
- Large language models (LLMs) for Knowledge-aware Recommender Systems
- Beyond-Accuracy Recommendation Quality:
- Using knowledge bases and knowledge graphs to increase recommendation=
quality(e.g., in terms of novelty, diversity, serendipity, or explainabili=
ty)
- Explainable Recommender Systems
- Knowledge-aware explanations to recommendations (compliant with the G=
eneral Data Protection Regulation)
- Online Studies:
- Using knowledge sources for cross-lingual recommendations
- Applications of knowledge-aware recommenders (e.g., music or news rec=
ommendation, off-mainstream application areas)
- User studies (e.g., on the user's perception of knowledge-based recom=
mendations), 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
- Large language models (LLMs) for Conversational Recommender Systems
- User Modeling and interfaces:
- Critiquing and user 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
We invite three kinds of submissions, which address novel issues in Knowled=
ge-aware and Conversational Recommender Systems:
* **Long Papers** should report on substantial contributions of lasting val=
ue. **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)**.
* **Short/Demo Papers** typically discuss exciting new work that is not yet=
mature enough for a long paper. In particular, novel but significant propo=
sals will be considered for acceptance in this category despite not having =
gone through sufficient experimental validation or lacking a strong theoret=
ical foundation. Applications of recommender systems to novel areas are esp=
ecially 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 referenc=
es)**.
* **Position/Discussion Papers** describe novel and innovative ideas. Posit=
ion 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 assum=
ptions, motivating the difficulty of a certain problem, or explaining why c=
urrent techniques are not sufficient, possibly corroborated by quantitative=
and qualitative arguments. **The Position/Discussion papers must have a le=
ngth of a minimum of 2 and a maximum of 3 pages (plus an unlimited number o=
f pages for references)**.
Papers may range from theoretical works to system descriptions.
We particularly encourage Ph.D. students or Early-Stage Researchers to subm=
it their research. We also welcome contributions from the industry and pape=
rs describing ongoing funded projects which may result useful to the Knowle=
dge-aware and Conversational Recommender Systems community.
Submission will be peer-reviewed and accepted papers will appear in the **w=
orkshop proceedings** (CEUR workshop series). The review process is **singl=
e-blind**. Submitted papers will be evaluated according to their originalit=
y, technical content, style, clarity, and relevance to the workshop.
Long and short/demo paper submissions must be original work and may not be =
under submission to another venue at the time of review. Each accepted long=
or short paper will be included in the CEUR online Workshop proceedings an=
d presented in a plenary session as part of the Workshop program.
Original position/discussion accepted papers will be included in the CEUR o=
nline Workshop proceedings. Selected position/discussion papers will be inv=
ited as oral presentations.
Submissions of full research papers must be in English, in PDF format in th=
e **CEUR-WS two-column conference format** available as compressed archive =
(http://ceur-ws.org/Vol-XXX/CEURART.zip)
or as Overleaf template (https://www.overleaf.com/latex/templates/template-=
for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/hpvjjzhjxzjk).
Papers must be submitted through EasyChair (https://easychair.org/conferenc=
es/?conf=3Drecsys2024workshops) by selecting the track "KaRS: Sixth Knowled=
ge-aware and Conversational Recommender Systems Workshop".
# Important Dates
* **Paper submissions deadline**: September 6th, 2024
* **Paper acceptance notification**: September 16th, 2024
* **Camera-ready deadline**: September 30th, 2024
* **Workshop day**: October 14th-18th, 2024
Deadlines refer to 23:59 (11:59 pm) in the AoE (Anywhere on Earth) time zon=
e.
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4th Call for Papers </div>
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Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS=
2024)</div>
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https://kars-workshop.github.io/2024/</div>
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; Oct. 14th - Oct. 18th, 2024, Bari</div>
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; DEADLINE EXTENDED - Submission deadline: September 6th, 2024, AoE</=
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
We are pleased to invite you to contribute to the Sixth Knowledge-aware and=
Conversational Recommender Systems Workshop held in conjunction with the A=
CM International Conference on Recommender Systems (RecSys 2024), Bari, Ita=
ly, from October the 14th to October
the 18th, 2024.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
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</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
# Scope</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Recommender systems have achieved ubiquity across various domains, ranging =
from e-commerce to media content suggestions, playing pivotal roles in faci=
litating user online experiences. However, despite their prevalence, these =
systems often encounter challenges
in effectively engaging with human users. While data-driven algorithms hav=
e demonstrated success in uncovering latent connections within user-item in=
teractions, they frequently overlook the central actor in this loop: the en=
d-user.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
A prevalent behavior among human users, which is rarely encoded in recommen=
dation engines, is the utilization of domain-specific knowledge. Fortunatel=
y, Knowledge-aware Recommender Systems are garnering increasing attention w=
ithin the recommendation community.
By leveraging explicit domain knowledge represented via ontologies or know=
ledge graphs, these approaches can understand the semantic relationships be=
tween users, items, and other entities, thus offering tailored recommendati=
ons to users and addressing inherent
limitations of purely data-driven systems. Despite their existence for ove=
r two decades, their significance has been revitalized due to the Linked Op=
en Data initiative and the availability of large knowledge-graphs such as D=
Bpedia and Wikidata.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Linked data and their ontologies underpin many recommendation approaches an=
d challenges proposed in recent years, such as Knowledge Graph embeddings, =
hybrid recommendation, link prediction, knowledge transfer, interpretable r=
ecommendation, and user modeling.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Moreover, a new wave in this domain is marked by the emergence of neuro-sym=
bolic systems, integrating data-driven methodologies with symbolic reasonin=
g. This fusion of machine learning systems, adept at harnessing data, with =
symbolic approaches, adept at leveraging
knowledge, holds promise in enhancing recommendation quality, particularly=
in scenarios with sparse training data.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
In parallel, the rise of Conversational Recommender Systems (CRSs) highligh=
ts the crucial role of content features in facilitating user interactions, =
particularly in multi-turn dialogues between users and the system, which br=
ing about novel challenges, such
as the incorporation of both short- and long-term preferences, prompt adap=
tation to user feedback, the limited availability of datasets, and the eval=
uation beyond simple accuracy metrics.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
In this context, the emergence of Large Language Models (LLMs) is pivotal a=
nd has breathed new life into CRSs. LLMs significantly impact CRSs by lever=
aging their advanced capabilities in understanding user queries and generat=
ing relevant recommendations in
natural language. They excel in processing complex and nuanced user inputs=
, allowing for more seamless and engaging interactions between users and th=
e system. Moreover, LLMs contribute to the adaptability and responsiveness =
of CRSs by continuously learning
from user interactions, thus refining their recommendations and improving =
the user experience over time.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
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</div>
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
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<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
The **Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) W=
orkshop** aims to spark a new generation of research that prioritizes user =
experience, engagement, and satisfaction over mere accuracy.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Drawing upon a multifaceted set of expertise including **Machine Learning**=
, **Deep Learning**, **Human-Computer Interaction**, **Information Retrieva=
l**, and **Information Systems**, this workshop endeavors to catalyze a fre=
sh wave of research.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
KaRS serves as a dynamic forum where researchers and practitioners, scholar=
s and industry professionals, converge to not only disseminate their latest=
findings but also to identify the emerging topics, delineate the next key =
challenges, and forecast the future
opportunities for research and development. </div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Through fostering active participation and facilitating the exchange of ide=
as, KaRS aims to cultivate an interdisciplinary community that collaborates=
on the topics of knowledge-aware and conversational recommenders, alongsid=
e the emerging topics of LLMs and
neuro-symbolic methodologies, which further extend the scope of this new e=
dition of KaRS.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
# Topics</div>
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
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<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
This workshop aims at establishing an interdisciplinary community with a fo=
cus on the exploitation of (semi-)structured knowledge and conversational a=
pproaches for recommender systems and promoting collaboration opportunities=
between researchers and practitioners.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Topics of interest include, but are not limited to:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- **Knowledge-aware Recommender Systems**.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Models and Feature Engineering:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Knowledge-aware data models based on structured knowledge s=
ources (e.g., Linked Open Data, BabelNet, Wikidata, etc.)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Semantics-aware approaches exploiting the analysis of textu=
al sources (e.g., Wikipedia, Social Web, etc.)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Knowledge-aware user modeling</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Methodological aspects (evaluation protocols, metrics, and =
data sets)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Logic-based modeling of a recommendation process</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Knowledge Representation and Automated Reasoning for recomm=
endation engines</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Deep learning methods to model semantic features</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Large language models (LLMs) for Knowledge-aware Recommende=
r Systems</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Beyond-Accuracy Recommendation Quality:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Using knowledge bases and knowledge graphs to increase reco=
mmendation quality(e.g., in terms of novelty, diversity, serendipity, or ex=
plainability)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Explainable Recommender Systems</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Knowledge-aware explanations to recommendations (compliant =
with the General Data Protection Regulation)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Online Studies:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Using knowledge sources for cross-lingual recommendations</=
div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Applications of knowledge-aware recommenders (e.g., music o=
r news recommendation, off-mainstream application areas)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- User studies (e.g., on the user's perception of knowledge-b=
ased recommendations), field studies, in-depth experimental offline evaluat=
ions</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- **Conversational Recommender Systems**.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Design of a Conversational Agent:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Design and implementation methodologies</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Dialogue management (end-to-end, dialog-state-tracker model=
s)</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- UX design</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Dialog protocols design</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Large language models (LLMs) for Conversational Recommender=
Systems</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- User Modeling and interfaces:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Critiquing and user feedback exploitation</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Short- and Long-term user profiling and modeling</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Preference elicitation</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Natural language-, multi-modal-, and voice-based interfaces=
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Next-question problem</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Methodological and Theoretical aspects:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Evaluation and metrics</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Datasets</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
- Theoretical aspects of conversational recommender systems</=
div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
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# Submissions</div>
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<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
We invite three kinds of submissions, which address novel issues in Knowled=
ge-aware and Conversational Recommender Systems:</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
* **Long Papers** should report on substantial contributions of lasting val=
ue. **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)**.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
* **Short/Demo Papers** typically discuss exciting new work that is not yet=
mature enough for a long paper. In particular, novel but significant propo=
sals will be considered for acceptance in this category despite not having =
gone through sufficient experimental
validation or lacking a strong theoretical foundation. Applications of rec=
ommender systems to novel areas are especially welcome. **The Short/Demo pa=
pers must have a length of a minimum of 3 and a maximum of 5 pages (plus an=
unlimited number of pages for references)**.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
* **Position/Discussion Papers** describe novel and innovative ideas. Posit=
ion 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 re=
search 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 le=
ngth of a minimum of 2 and a maximum of 3 pages (plus an unlimited number o=
f pages for references)**. </div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
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<br>
</div>
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Papers may range from theoretical works to system descriptions.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
We particularly encourage Ph.D. students or Early-Stage Researchers to subm=
it their research. We also welcome contributions from the industry and pape=
rs describing ongoing funded projects which may result useful to the Knowle=
dge-aware and Conversational Recommender
Systems community.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Submission will be peer-reviewed and accepted papers will appear in the **w=
orkshop proceedings** (CEUR workshop series). The review process is **singl=
e-blind**. Submitted papers will be evaluated according to their originalit=
y, technical content, style, clarity,
and relevance to the workshop.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Long and short/demo paper submissions must be original work and may not be =
under submission to another venue at the time of review. Each accepted long=
or short paper will be included in the CEUR online Workshop proceedings an=
d presented in a plenary session
as part of the Workshop program.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Original position/discussion accepted papers will be included in the CEUR o=
nline Workshop proceedings. Selected position/discussion papers will be inv=
ited as oral presentations.</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Submissions of full research papers must be in English, in PDF format in th=
e **CEUR-WS two-column conference format** available as compressed archive =
(http://ceur-ws.org/Vol-XXX/CEURART.zip) </div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
or as Overleaf template (https://www.overleaf.com/latex/templates/template-=
for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/hpvjjzhjxzjk).=
</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Papers must be submitted through EasyChair (https://easychair.org/conferenc=
es/?conf=3Drecsys2024workshops) by selecting the track "KaRS: Sixth Kn=
owledge-aware and Conversational Recommender Systems Workshop".</div>
<div style=3D"font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, =
Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
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# Important Dates</div>
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<br>
</div>
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* **Paper submissions deadline**: September 6th, 2024</div>
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* **Paper acceptance notification**: September 16th, 2024</div>
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Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
* **Camera-ready deadline**: September 30th, 2024</div>
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* **Workshop day**: October 14th-18th, 2024</div>
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<br>
</div>
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nt, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; c=
olor: rgb(0, 0, 0);">
Deadlines refer to 23:59 (11:59 pm) in the AoE (Anywhere on Earth) time zon=
e.</div>
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