Information Retrieval: Basics and Techniques

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Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.

Covers TF-IDF computation, document vectors, cosine similarity, and precision formulas.

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.

Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.