Set-oriented models (boolean models, fuzzy set model, extended boolean model), algebraic models (vector space models, latent semantic indexing model, topic models), probabilistic models (classical and language models).
Web information retrieval and its peculiarities.
Web search engines (crawler, indexer). HITS algorithm (Hyperlink-induced topic search). Google search engine (the PageRank metric). The SALSA algorithm, variants in web searching
Machine Learning Techniques and Neural Models in Information Retrieval (Learning to Rank, vector representation of words and word embeddings such as word2vec, CBOW, skipgram, Transformers, BERT, GPT, Large Language Models and sparse vs dense search, Vector Search as in e.g. FAISS (HNSW etc.), using dense and/or sparse search in Retrieval Augmented Generation (RAG), Search Engines against Reasoning Engines).
Storage Techniques in Distributed Information Retrieval (MapReduce, Apache Spark)
Full indexing structures in main memory (suffix trees, suffix arrays, acyclic directed graphs (DAWG) for strings), and in secondary memory (supra-suffix array, prefix Β-tree, string Β-tree).
Compression algorithms for text and for indexing structures.
Text Mining and Graph based Models (graph embeddings).
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