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Understood and implement the scoring algo BM 25
- Addressing the need to find relevant information buried in a pile of documents, the article discusses the importance of assigning relevance scores to documents based on search queries.
- Introduces the concept of tokens in documents and the creation of an inverted index to associate tokens with the documents they appear in.
- Explains the initial scoring algorithm based on token frequency in documents, highlighting the need to address issues such as token count and relevancy.
- Proposes an enhanced scoring function considering all query tokens, limiting the impact of individual tokens, and boosting results matching multiple tokens.
- Introduces the concept of diminishing returns to prevent a linear score increase based on token frequency, providing a more nuanced scoring approach.
- Discusses the limitations of the TF approach and introduces the TF-IDF method to incorporate the rarity of words across all documents in the score calculation.
- Further advances the scoring algorithm by introducing the BM25 method, which considers factors like term frequency, document length, and normalization to determine document relevance.
- Detailed examples and calculations are provided to demonstrate how BM25 scoring works and how it can be implemented effectively in code.
- The article concludes by highlighting the importance of adapting the scoring algorithm to consider various document attributes like title, date, and location for improved relevance ranking.
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