Ranking (information retrieval)

Ranking Algorithms - PageRank: Originated in the 1940s, developed by Wassily Leontief in economics. - Endorsement Ranking: Developed by Charles H Hubbell in 1965, based on the importance of endorsers. - Journal Ranking: Developed by Gabriel Pinski and Francis Narin, based on citations from important journals. - HITS Algorithm: Developed by Jon Kleinberg, treating web pages as hubs and authorities. Ranking Models - Boolean Model: Fetches complete matches, does not rank documents. - Vector Space Model: Addresses partial matches, assigns weights to index items, calculates similarity scores using cosine similarity. - Probabilistic Model: Uses probability theory, ranks documents based on decreasing probability of relevance. Evaluation Measures - Precision: Measures the proportion of top-ranked results that are relevant. - Recall: Measures the completeness of the information retrieval process. - F1 Score: Combines precision and recall into a harmonic mean. - Precision-Recall Curves: Plotted to evaluate ranked retrieval results. HITS Algorithm - HITS uses Link Analysis for analyzing page relevance. - Works on small sets of subgraph. - Query dependent. - Subgraphs are ranked according to weights in hubs and authorities. - Pages with the highest ranks are fetched and displayed. Learning to Rank: Application of Machine Learning - Learning to rank is an application of machine learning. - Used for solving the ranking problem. - Machine learning techniques are applied to rank items. - Widely used in information retrieval. - Learning to rank improves the accuracy of search results.