The probabilistic relevance model[1] [2] 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(dj,q)=
| P(R|\vec{d | |
| j)}{P(\bar{R}|\vec{d} |
j)}
There are some limitations to this framework that need to be addressed by further development:
To address these and other concerns, other models have been developed from the probabilistic relevance framework, among them the Binary Independence Model from the same author. The best-known derivative of this framework is the Okapi (BM25) weighting scheme, along with BM25F, a modification thereof.