Relevance in Recommender Systems#
How well a recommended item matches a user’s interests.
Important
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What it is#
Relevance is whether a recommended item actually matches the user’s tastes and needs — an item they would find useful and want to engage with. It is the property that accuracy metrics (precision, recall, NDCG, MAP) are built to measure.
The traditional goal#
Recommend as many relevant items as possible, maximizing accuracy. For a long time this was the sole objective of recommender systems.
Not enough alone#
A perfectly relevant list can still be boring — ten near-identical hits the user already knows. So relevance is balanced against novelty, diversity and coverage, and modern novelty / diversity metrics are made relevance-aware (rewarding items that are novel and relevant) so a system is not credited for surfacing surprising-but-useless items. The aim is relevant *and* diverse.
Theme: Recommender Systems · All terminology
Hint
Mind map — connected ideas
Long-Tail Items · Self-Information of Popularity · Intra-List Diversity (ILD) · Catalog Coverage · Cosine Similarity of Item Features · Dominating in Recommender Systems
Hint
More in Recommender Systems
Catalog Coverage · Cosine Similarity of Item Features · Diminishing Utility · Diversity (in Recommender Systems) · Dominating in Recommender Systems · Genre Overlap · Hit Rate (HR) · Intra-List Diversity (ILD) · Item Coverage · Jaccard index · Novelty (in Recommender Systems) · Self-Information of Popularity · User Coverage
See also
Source article Adapted (context, re-expressed) in our own words from: Relevance in Recommender Systems (insightful-data-lab.com).