A user-controlled exploration layer over semantic movie clusters reduces recommendation redundancy (ILS 0.34 to 0.26) but collapses relevance (NDCG 0.00), earning preference from simulated long-history LLM users in 72.7% of cases.
On unexpectedness in recommender systems: Or how to better expect the unexpected,
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Exploration on Demand: From Algorithmic Control to User Empowerment
A user-controlled exploration layer over semantic movie clusters reduces recommendation redundancy (ILS 0.34 to 0.26) but collapses relevance (NDCG 0.00), earning preference from simulated long-history LLM users in 72.7% of cases.