Crate rbo

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The RBO indefinite rank similarity metric.

This code implements the RBO metric, as described in:

@article{wmz10:acmtois,
    author = "Webber, William and Moffat, Alistair and Zobel, Justin",
    title = "A similarity measure for indefinite rankings",
    journal = "ACM Transactions on Information Systems",
    year = {2010},
}

What is RBO (taken from the paper)

The rank-biased overlap (RBO) measure is based on a simple probabilistic user model. This measure is based on (but is not tied to) a simple user model in which the user compares the overlap of the two rankings at incrementally increasing depths. The user has a certain level of patience, parameterized in the model, and after examining each depth has a fixed probability of stopping, modelled as a Bernoulli random variable. RBO is then calculated as the expected average overlap that the user observes in comparing the two lists. The measure takes a parameter that specifies the user’s persistence p, that is, the probability that the user, having examined the overlap at one rank, continues on to consider the overlap at the next.

The (convergent) sum of the weights of the (potentially infinite) tail determines the gap or residual between the minimum and maximum similarity scores that could be attained on exhaustive evaluation. The minimum, maximum, and residual scores on partial RBO evaluation are all monotonic in depth. A point score can also be extrapolated.

Correctness

This code tests against the original rbo_ext implementation by William Webber and against another reference implementation for rbo_min and rbo_res.

Example:

use rbo::rbo;

let first = "abcdefghijklmnopqrstuvwxyz".chars().collect::<Vec<_>>();
let second = "kxcnarvmwyp".chars().collect::<Vec<_>>();
let rbo_val = rbo(&first,&second,0.99).expect("valid rbo");
println!("{}",rbo_val);

Crate rbo

What is RBO (taken from the paper)

Correctness

Example:

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