deduplicationranking

Deduplication ranking is the process of producing an ordered list of items from a corpus while removing or suppressing duplicates or near-duplicates. The goal is to improve result quality, diversity, and user satisfaction by avoiding repetitive content and reducing presentation of redundant information. It is applied in search engines, document retrieval, recommender systems, and data curation tasks.

Two common design choices exist. In deduplication-aware ranking, duplicates are detected and filtered as part of the ranking process, ensuring the final top-k set contains distinct items. In post hoc deduplication, a standard ranking is produced first, and duplicates are suppressed or consolidated when selecting the final results.

Techniques for detecting duplicates include fingerprinting, shingling, MinHash, and SimHash; clustering of similar items; and threshold-based or supervised classification to identify duplicates. Ranking can then be adjusted by: assigning a single representative from each duplicate group, penalizing repeated content, or applying diversification objectives such as Maximal Marginal Relevance (MMR) to favor novel items. If diversity is preferred but strict deduplication is not, methods that optimize for both relevance and distinctiveness can be used.

Applications include web search, enterprise search, e-discovery, and multimedia retrieval. Evaluation uses traditional ranking metrics like precision, recall, and NDCG computed over deduplicated results, as well as distinct-aware metrics that measure the coverage of unique content. Challenges include accurate near-duplicate detection, choosing appropriate similarity thresholds, computational overhead, evolving catalogs, and privacy concerns when deduplicating personal data.

See also: deduplication, diversification, near-duplicate detection.