Speaker
Description
Scholastic corpora present distinctive challenges for recommender systems because of their deeply nested textual hierarchies. In such contexts, identifying related texts is insufficient; recommendations must also operate at an appropriate level of granularity, matching the user’s current focus without being overly narrow or excessively broad. This talk addresses the granularity matching problem by expanding the notion of a “document” and generating multiple types of embeddings across hierarchical levels, enabling similarity to be modeled at different conceptual scales. It further suggests that effective recommendation in these corpora is not only a retrieval problem but also a user-interface problem and demonstrates how interface design can support contextualized and transparent recommendations.