SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations

SPADE (Serendipitous Pareto Distance Evaluation) is a new metric for recommender systems that simultaneously considers item similarity, popularity, and user relevance. It projects items into a two‑dimensional space and computes a user‑specific Pareto frontier of maximally popular and historically similar items, then averages the minimum Euclidean distance from this frontier for correctly recommended test‑set items. Experiments on five datasets and five baseline algorithms demonstrate that SPADE effectively discourages algorithms from exploiting accuracy‑only metrics and reliably isolates serendipitous discoveries.

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