A Knowledge-Based Book Recommender through Enhanced Retrieval-Augmented Generation [* Cross-Reference *]

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Vlada Khomenko; Christoph Schommer; Salima Lamsiyah, 2026, "A Knowledge-Based Book Recommender through Enhanced Retrieval-Augmented Generation [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.ULRWFF, LIST Open Repository, V1
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Description
Recommender systems (RS) address choice overload and reduce friction in discovering new items by providing users with suggestions that match their interests. Traditional RS, which rely on user-item interaction history or structured metadata, often struggle with cold-start scenarios and limited transparency. This paper addresses these limitations by proposing a knowledge-based book recommendation system that operates on unstructured natural-language item descriptions. Leveraging the capabilities of large language models (LLMs), the system employs a retrieval-augmented generation (RAG) architecture with a hybrid semantic-lexical retrieval setup. It enhances retrieval through query expansion, topic filtering, and cross-encoder re-ranking, and integrates chain-of-thought (CoT) prompting for more predictable, transparent, and structured generation. The system is comprehensively evaluated across retrieval, generation, and user-centered metrics. Results demonstrate that each proposed enhancement contributes incrementally to the system performance, while user feedback confirms its usefulness and the value of transparent natural-language explanations. Overall, this work demonstrates the feasibility and effectiveness of a cold-start-capable, RAG-based recommendation approach that leverages unstructured data, providing a foundation for broader applications across other domains. A demo of the system is availablea . (2026-01-01)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5220/0014480000004052 for the original and latest version of the publication*** (2026-07-01)
Subject Computer and Information Science; Physics
Keyword Recommender system, Matching (statistics), Feature (linguistics), Key (lock)
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