Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years.
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e. g., product images or descriptions) as items' side information to improve recommendation accuracy.
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF).
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph.
Motivated by the outlined aspects, we propose \framework, a unified framework for the extraction of multimodal features in recommendation.
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios.
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence.
We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations.
Hyper-parameters tuning is a crucial task to make a model perform at its best.