Learning to Rank
I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgement for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
applications
Information Retrieval
Ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising. A possible architecture of a machine learned search engine is shown in the figure below.
Training data consists of queries and documents matching them together with the relevance degree of each match. It may be prepared manually by human accessors (or raters, as Google calls them), who check results for some queries and determine the relevance of each result. It is not possible to check the relevance of all documents, so typically a technique called pooling is used - only the top few documents retrieved by some existing ranking models are checked. Alternatively, training data may be derived automatically by analyzing clickthrough logs (search results which got clicks from users), query chains, or search engines' features.
Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries.
For convenience of MLR algorithms query-document pairs are usually represented by numerical vectors, which are called feature vectors. Such an approach is sometimes called bag of features and is analogous to the bag of words model and vector space model used in information retrieval for representation of documents.
Components of such vectors are called features, factors or ranking signals. They may be divided into three groups:
- query-independent or static features - those features which depend only on the document, but not on the query
- query-dependent or dynamic features - which depend on the contents of the document and the query
- query-level features which depend only on the query
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