Interest Representation

Interest representation in recommender systems focuses on accurately capturing users' diverse and evolving preferences to improve the relevance and personalization of recommendations. Current research emphasizes developing sophisticated models that disentangle multiple interests, model their dynamics over time (both short-term and long-term), and incorporate various data modalities (e.g., visual, textual) to create richer user profiles. These advancements, often leveraging neural network architectures like attention mechanisms and generative models, aim to address data sparsity and cold-start problems, leading to more effective and efficient recommendation systems across various applications. The resulting improvements in recommendation accuracy and user experience have significant implications for businesses and online platforms.