BM25 Ranking
Retrieval & Ranking Systems DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding BM25 Ranking, Term Frequency Saturation, Document Length Normalization, Inverse Document Frequency, Query Expansion, BM25 Hyperparameters, Information Retrieval, Natural Language Processing, Probability Theory, Data Structures, Algorithm Design, Ranking Functions, Text Preprocessing, Statistical Modeling, Inverted Indexing, Computational Complexity.
Implement the BM25 ranking function to calculate document scores for a query in an information retrieval context. BM25 is an advanced variation of TF IDF that incorporates term frequency saturation, document length normalization, and a configurable penalty for document length effects.