• OwenShap

    In this repository you find the code used in the paper: ``A Multilinear Sampling Algorithm to Estimate Shapely Values``.

    Github repository: https://aldolipani.github.io/OwenShap

  • sRBP

    In this repository you find the code used in the paper: ``From a User Model for Query Sessions to Session Rank Biased Precision (sRBP)``.

    Github repository: https://aldolipani.github.io/sRBP

  • Probabilistic Models

    In this repository you find the implementation of the probabilistic models analysed in the paper: ``A systematic approach to normalization in probabilistic models``.

    Github repository: https://aldolipani.github.io/ProbabilisticModels

  • Pool Bias Estimators

    In this repository you find the implementation of the pool bias estimators analysed in the papers, ``Splitting Water: Precision and Anti-Precision to Reduce Pool Bias`` and ``The Curious Incidence of Bias Corrections in the Pool``.

    With Pool bias in Information Retrieval (IR) is meant the bias of a test collection against new systems that appears as side-effect of the use of the pooling technique. In this repository we find the implementation of the pool bias estimators analyzed in the SIGIR-2015 paper: “Splitting Water: Precision and Anti-Precision to Reduce Pool Bias” and in the ECIR-2016 paper: “The Curious Incidence of Bias Corrections in the Pool”.

    The application has two use cases, analysis of the test collection and run bias correction.

    The available IR measures are: P@10, P@15, P@20, P@30 and P@100.

    Source code and documentation available here.

    Version 2.0

    Version of the application used to generate the results in the ECIR 2016 paper: "The Curious Incidence of Bias Corrections in the Pool".

    Version 1.0

    Version of the application used to generate the results in the SIGIR 2015 paper: "Splitting Water: Precision and Anti-Precision to Reduce Pool Bias".