Evaluating the Impacts of Combining Embedding Extraction Techniques with Transformer Models for Music Recommendation - Simm, Vinicius S.; Leme, Matheus H. C.; Tanno, Douglas R.; Domingues, Marcos A. (Anais do Seminário Integrado de Software e Hardware (SEMISH); 2026: Anais do LIII Seminário Integrado de Software e Hardware, 2026)
This paper investigates the impact of different embedding strategies on Transformer-based models for session-based music recommendation. We compared two approaches: a standard Transformer model that learns item embeddings internally, and a second model that uses pre-trained embeddings from Word2Vec (CBOW and Skip-Gram), generating recommendations via cosine similarity. We evaluated on two music datasets, Music4All and Xiami, and our results highlight a trade-off between accuracy and diversity. The Transformer model achieves a significantly higher Hit Rate but shows a stronger popularity bias and lower catalog coverage. On the other hand, the approach using pre-trained embeddings shows greater coverage and diversity but lower accuracy.