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Query-level Feature
A query-level feature or QLF is a ranking feature utilized in a machine-learned ranking algorithm. Example QLFs: * How many times has this query been run in the last month? * How many words are in the query? * What is the sum/average/min/max/median of the BM25F values for the query? {{compu-ai-stub Machine learning algorithms ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine-learned Ranking
Learning to rank. Slides from Tie-Yan Liu's talk at WWW 2009 conference aravailable online 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 consists 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 judgment (e.g. "relevant" or "not relevant") 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 In 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 accompanying figure. Training da ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probabilistic Relevance Model (BM25)
The probabilistic relevance model was devised by Stephen E. Robertson and Karen Spärck Jones as a framework for probabilistic models to come. It is a formalism of information retrieval useful to derive ranking functions used by search engines and web search engines in order to rank matching documents according to their relevance to a given search query. It is a theoretical model estimating the probability that a document ''dj'' is relevant to a query ''q''. The model assumes that this probability of relevance depends on the query and document representations. Furthermore, it assumes that there is a portion of all documents that is preferred by the user as the answer set for query ''q''. Such an ideal answer set is called ''R'' and should maximize the overall probability of relevance to that user. The prediction is that documents in this set ''R'' are relevant to the query, while documents not present in the set are non-relevant. sim(d_,q) = \frac Related models There are s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
