Rating Matrix

Rating matrices, representing user preferences or item properties, are central to various applications, from recommender systems to dialogue evaluation and even sports analytics. Current research focuses on improving matrix completion techniques, often leveraging graph neural networks or incorporating additional information like social networks, textual reviews, or ordinality of ratings to enhance prediction accuracy and efficiency. These advancements are impacting fields like personalized recommendations, dialogue system design, and the quantification of subjective properties where direct numerical measurement is unavailable. The development of more robust and efficient algorithms for handling large-scale rating data is a key ongoing theme.