fca-list August 2024

fca-list@cs.uni-kassel.de

3 participants
3 discussions

Workshop FCA4AI 2024 (Deadline extension)

by Amedeo Napoli

Sorry for duplicates. ------------------------------------------------------------------------------------------------------------------- -- FCA4AI (Twelfth Edition) -- "What can FCA do for Artificial Intelligence?'' co-located with ECAI 2024, Santiago de Compostela, Spain October 19th 2024 http://www.fca4ai.hse.ru/2024 ------------------------------------------------------------------------------------------------------------------- General Information. The eleven preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2023) showed that many researchers working in Artificial Intelligence are indeed interested in powerful techniques for classification and data mining provided by Formal Concept Analysis. The twelfth edition of FCA4AI will once more be co-located with the ECAI Conference, and thus be held in Santiago de Compostela. Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications and association rules) which can be used for many AI needs, e.g. knowledge processing, knowledge discovery, knowledge representation and reasoning, ontology engineering as well as information retrieval, recommendation, social network analysis and text processing. Thus, there are many "natural links'' between FCA and AI. Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of plain FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis, as well as on hybridization with other formalisms. These extensions are aimed at allowing FCA to deal with more complex than just binary data, for solving complex problems in data analysis, classification, knowledge processing, etc. While the capabilities of FCA are extended, new possibilities are arising in the framework of FCA. As usual, the FCA4AI workshop is dedicated to the discussion of such issues, and in particular: - How can FCA support AI activities in knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing and others? - Vice versa, how can the current developments in AI be adopted within FCA to help AI researchers solve complex problems in their domain? - Which role can FCA play in the new trends in AI, especially in ML, XAI, fairness of algorithms, and "hybrid systems'' combining symbolic and subsymbolic approaches? TOPICS OF INTEREST include but are not limited to: - Concept lattices and related structures: pattern structures, relational structures, distributive lattices. - Knowledge discovery and data mining: pattern mining, association rules, attribute implications, subgroup discovery, exceptional model mining, data dependencies, attribute exploration, stability, projections, interestingness measures, MDL principle, mining of complex data, triadic and polyadic analysis. - Knowledge and data engineering: knowledge representation, reasoning, ontology engineering, mining the web of data, text mining, data quality checking. - Analyzing the potential of FCA in supporting hybrid systems: how to combine FCA and data mining algorithms, such as deep learning for building hybrid knowledge discovery systems, producing explanations, and assessing system fairness. - Analyzing the potential of FCA in AI tasks such as classification, clustering, biclustering, information retrieval, navigation, recommendation, text processing, visualization, pattern recognition, analysis of social networks. - FCA and Large Language Models (LLMs). - Practical applications in agronomy, astronomy, biology, chemistry, finance, manufacturing, medicine, and others. The workshop will include time for audience discussion aimed at a better understanding of the issues, challenges, and ideas being presented. IMPORTANT DATES: Submission deadline: September 08 2024 Notification to authors: September 23 2024 Final version: October 06 2024 Workshop: October 19 2024 SUBMISSION DETAILS: The workshop welcomes submissions in pdf format following the CEURART style 1-column (to be downloaded at https://ceur-ws.org/Vol-XXX/CEURART.zip). Submissions can be: - technical papers between 8 and 12 pages, - system descriptions or position papers describing work in progress not exceeding 6 pages. Submissions are via EasyChair at https://easychair.org/conferences/?conf=fca4ai2024 The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-3489, Vol-3233, Vol-2972, Vol-2729, Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939). WORKSHOP CHAIRS: Sergei O. Kuznetsov National Research University Higher Schools of Economics, Moscow, Russia Amedeo Napoli Université de Lorraine, CNRS, Inria, LORIA, Nancy, France Sebastian Rudolph Technische Universität Dresden, Germany PROGRAM COMMITTEE (under construction) ------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------

5 months, 1 week

Second Rostock Open Science Workshop

by MPIDR - Career

Open science aims to make research results and materials freely accessible to everyone, with the goal of increasing knowledge circulation, increasing transparency, and providing the means to reproduce and generalize published findings. Venue: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany Dates: March 17-18, 2025 Website: https://www.demogr.mpg.de/en/news_events_6123/calendar_1921/second_rostock_… -- This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance.

6 months

[KaRS 2024] 3rd CfP: 6th Knowledge-aware and Conversational Recommender Systems Workshop 2024 @ ACM RecSys

by Vito Walter Anelli

3rd 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 Submission deadline: August 30th, 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 ACM International Conference on Recommender Systems (RecSys 2024), Bari, Italy, 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 facilitating 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 latent 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 recommendation engines, is the utilization of domain-specific knowledge. Fortunately, Knowledge-aware Recommender Systems are garnering increasing attention within the recommendation community. By leveraging explicit domain knowledge represented via ontologies or knowledge graphs, these approaches can understand the semantic relationships between users, items, and other entities, thus offering tailored recommendations to users and addressing inherent limitations of purely data-driven systems. Despite their existence for over two decades, their significance has been revitalized due to the Linked Open Data initiative and the availability of large knowledge-graphs such as DBpedia and Wikidata. Linked data and their ontologies underpin many recommendation approaches and challenges proposed in recent years, such as Knowledge Graph embeddings, hybrid recommendation, link prediction, knowledge transfer, interpretable recommendation, and user modeling. Moreover, a new wave in this domain is marked by the emergence of neuro-symbolic systems, integrating data-driven methodologies with symbolic reasoning. 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. In parallel, the rise of Conversational Recommender Systems (CRSs) highlights the crucial role of content features in facilitating user interactions, particularly in multi-turn dialogues between users and the system, which bring about novel challenges, such as the incorporation of both short- and long-term preferences, prompt adaptation to user feedback, the limited availability of datasets, and the evaluation beyond simple accuracy metrics. In this context, the emergence of Large Language Models (LLMs) is pivotal and has breathed new life into CRSs. LLMs significantly impact CRSs by leveraging their advanced capabilities in understanding user queries and generating relevant recommendations in natural language. They excel in processing complex and nuanced user inputs, allowing for more seamless and engaging interactions between users and the 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. The **Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop** 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 Retrieval**, and **Information Systems**, this workshop endeavors to catalyze a fresh wave of research. KaRS serves as a dynamic forum where researchers and practitioners, scholars 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. Through fostering active participation and facilitating the exchange of ideas, KaRS aims to cultivate an interdisciplinary community that collaborates on the topics of knowledge-aware and conversational recommenders, alongside the emerging topics of LLMs and neuro-symbolic methodologies, which further extend the scope of this new edition of KaRS. # 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 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 engines - 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 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 - 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 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)**. * **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)**. * **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)**. 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. Submission will be peer-reviewed and accepted papers will appear in the **workshop proceedings** (CEUR workshop series). The review process is **single-blind**. Submitted papers will be evaluated according to their originality, 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 and presented in a plenary session as part of the Workshop program. Original position/discussion accepted papers will be included in the CEUR online Workshop proceedings. Selected position/discussion papers will be invited as oral presentations. Submissions of full research papers must be in English, in PDF format in the **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-w…). Papers must be submitted through EasyChair (https://easychair.org/conferences/?conf=recsys2024workshops) by selecting the track "KaRS: Sixth Knowledge-aware and Conversational Recommender Systems Workshop". # Important Dates * **Paper submissions deadline**: August 30th, 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 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. 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6 months

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fca-list August 2024