Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
Authors: 
Vito Walter Anelli, 
Antonio Ferrara, 
Cataldo Musto, 
Fedelucio Narducci, 
Azzurra Ragone, 
Markus ZankerAuthors Info & Claims
Pages 1245 - 1249
Abstract
Recommender systems, though widely used, often struggle to engage users effectively. While deep learning methods have enhanced connections between users and items, they often neglect the user’s perspective. Knowledge-based approaches, utilizing knowledge graphs, offer semantic insights and address issues like knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. More recently, neural-symbolic systems, combining data-driven and symbolic techniques, show promise in recommendation systems, especially when used with knowledge graphs. Moreover, content features become vital in conversational recommender systems, which demand multi-turn dialogues. Recent literature highlights increasing interest in this area, particularly with the emergence of Large Language Models (LLMs), which excel in understanding user queries and generating recommendations in natural language. Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop aims to disseminate advancements and discuss about challenges and opportunities.
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Index Terms
- Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
Recommendations
Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
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Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsRecommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users ...
Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
RecSys '21: Proceedings of the 15th ACM Conference on Recommender SystemsIn 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 ...
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
Copyright © 2024 Owner/Author.
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
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Published: 08 October 2024
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