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- Data-Driven Recommendation Systems for Training Optimization in Indoor Team SportsPublication . Gonçalves, Luísa Fanado; Silva, Bruno Miguel Correia da; Travassos, Bruno Filipe RamaCurrently, significant technological advances are being made in the field of artificial intelligence. In particular, machine learning is transforming the way we process data and extract useful knowledge in areas such as healthcare, industry, and sports. In the sports context, analyzing sensor and video data allows you to model performance, detect patterns, and anticipate risks. However, analysis alone does not decide what to do next in training. Coaches still have to select, sequence, and adapt exercises under time and operational constraints. This is where recommendation systems add value: they transform analytical signals and historical preferences into suggestions tailored to the athletes’ needs and the context of the session. Indoor team sports, such as futsal or basketball, are dynamic and strategic games where cooperation between team members is crucial to success. Basic characteristics such as speed of play, precise coordination, and tactical strategy give these sports their unique character. In this context, there is a clear gap: specific recommendation solutions for indoor sports at the moment are rare. This dissertation proposes and evaluates recommender systems to support training planning, capable of suggesting personalized exercises and even complete training plans. The system integrates three complementary components: (i) matrix factorization based on implicit feedback, (ii) temporal co-occurrence modeling sequences of exercises within a session, and (iii) a content-based component using cosine similarity. The scores are combined in a hybrid model, with weights optimized during validation and with eligibility and context filters to ensure practical recommendations. We developed a pipeline covering data preparation for exercises and contextual information, model generation, hyperparameter optimization, and hybrid weight optimization. Finally, we evaluate the model considering two sports modalities and two temporal protocols, Hold-Out and Next-Step, reporting Recall@N, NDCG@N, Coverage@N, Diversity@N, HR@N, and MRR@N. The performance evaluation and results demonstrate the feasibility of recommender systems to support training planning in indoor team sports, paving the way for more personalized training plans and progressive integration with contextual data in future work.