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search.md

Search

Learning to Rank

  • Applies supervised machine learning to search relevance ranking.
  • A model is trained based on some preprocessed data.
  • This data with hand-labeled ranks is then used to optimize future results.

Vector Based Search

  • Each word is represented as a numerical quantity known as vector, using tools like word2vec (Google) or GloVe (Stanford).
    • This representation is called a text (word) embedding.
  • Each vector captures information about what a word means (semantics).
  • Words with similar meanings get similar vectors and can be projected onto a 2-dimensional graph for easy visualization.
  • These vectors are trained by ensuring words with similar meanings are physically near each other.

Query

  • Query expansion is where the query is broadened by introducing additional tokens or phrases.
  • Query relaxation is where tokens are ignored to make the query less restrictive and increase recall.
  • Query translation is where a text-translation paradigm is applied to convert tail (i.e., unpopular, 20th percentile) queries into head queries that account for the bulk of traffic, helping to increase recall.

Metrics

  • Precision is the fraction of relevant search results among the total search results.
    • If the engine returns 30 results, out of which 15 are relevant, then precision in is 15/30.
  • Recall is the ability of a search engine to retrieve all the relevant results from the corpus.
    • If there are 50 documents that are relevant, but the search engine returns only 30 of those, then the recall is 30 out of 50.
  • F score (specifically F1 score) is a single number, that represents both precision and recall.
  • Mean Reciprocal Rank (MRR) guides the search engine to put the most-desired result on the top.
    • It gives the score 1 for clicking the first result, ½ for clicking the second result, ⅓ for the third result, and so on.
  • Mean Average Precision (MAP) allows to quantify the relevance of the top returned results.
  • Normalized Discounted Cumulative Gain (nDCG) is like MAP, but weights the relevance of the result by its position.

Misc

  • Corpus is the complete set of documents that need to be searched.
  • Shingles are word-ngrams. Given a stream of tokens, the shingle filter will create new tokens by concatenating adjacent terms.
    • They give you the ability to pre-bake phrase matching. By building phrases into the index, you can avoid creating phrases at query time and save some processing time/speed.
    • The downside is that you have larger indices and potentially more memory usage.
  • Named-entity recognition (NER) locates, classifies and extracts named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations etc.
    • A named entity is a real-world object, such as persons, locations, organizations, products, etc.
    • In the sentence "Biden is the president of the United States", both "Biden" and the "United States" are named entities.
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