Term Level Knowledge

Term-level knowledge focuses on leveraging the semantic meaning and contextual information of individual words or terms to improve information retrieval and recommendation systems. Current research emphasizes incorporating this knowledge through advanced techniques like contextual embeddings (e.g., from BERT) and novel algorithms that efficiently combine term-level and higher-level feedback, such as contextual bandit approaches. These advancements aim to enhance the accuracy and efficiency of systems by more effectively capturing user preferences and relevant information within large datasets, impacting fields like search engines and recommender systems.