{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:54Z","timestamp":1782846234638,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>The exponential growth of digital music resources requires advanced retrieval methods beyond traditional metadata. These limitations are particularly evident when correlating disparate domains, such as acoustic features and literary texts, where the high dimensionality of data poses significant computational challenges. This paper presents an innovative computational system that maps musical pieces to narrative passages by simulating human-like cognitive correlations. The architecture utilises three transformer models for feature extraction: all-MiniLM-L6-v2 for vector embeddings, RoBERTa for emotion classification, and DistilBERT for sentiment analysis. To address the substantial computational cost of processing a large-scale corpus of 649,078 tracks, a novel Cascaded Filtering Architecture was implemented. This multi-stage approach serves as a necessary optimisation to ensure system interactivity and second-level responsiveness. All strategies employ vector metrics, including cosine similarity and Euclidean distance, to quantify proximity in a constructed joint space. Experimental results demonstrate that the cascade approach achieves up to a 16-fold increase in computational efficiency compared to standard hybrid models, while maintaining high recommendation relevance across diverse narrative contexts. This work highlights the potential of scalable multimodal matching, offering new perspectives for interactive, high-performance recommendation systems in the digital literature and media.<\/jats:p>","DOI":"10.7148\/2026-0652","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:43Z","timestamp":1782842923000},"page":"652-659","source":"Crossref","is-referenced-by-count":0,"title":["Music recommendation system for narrative texts based on semantic and emotional analysis"],"prefix":"10.7148","author":[{"given":"Jakub","family":"Pedryc","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filip","family":"Kruzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:45Z","timestamp":1782842925000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0652_dis_ecms2026_0041.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0652","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}