Semantic Tag Clustering to Alleviate the Cold Start Problem in Learning Resource Recommendation: A Case Study on Delicious Dataset

Abstract

This study explores a methodology for recommending learning resources, demonstrated through a case study on the Delicious dataset. Tags, representing keywords assigned to describe content, are semantically clustered using K-Means. Sentence Transformers are employed to generate dense vector representation of these tags, enabling more effective clustering. The system identifies meaningful tag groups to deliver relevant recommendations, even in the absence of user interaction history, effectively addressing the cold-start problem through predefined tag profiles. The proposed methodology personalizes resource recommendations for Deaf and Hard of Hearing (DHH) learners by leveraging their profile and resource Meta data. It enhances resource search during the cold start phase by identifying the most relevant tag cluster that matches with the learner’s search query and retrieving preferred content based on the learner profile.  Future extensions could incorporate dynamic preferences that evolve over time, enabling more adaptable and personalized recommendations. This work provides a robust foundation for clustering the resources based on their semantic meaning, thereby improving content-based search and retrieval of relevant learning resources