PyClick is an open-source Python library of click models for web search. It implements most of standard click models and inference methods.

cd $PROJECT_DIR
sudo python setup.py install

For running the code:

• numpy 1.7
• enum34

For unit-testing:

• nose
• nose_parameterized

Examples are located in the examples folder. Data samples are in examples/data. Examples can be run as follows:

python examples/SimpleExample.py $CLICK_MODEL examples/data/YandexRelPredChallenge $SESSION_NUM

Here, $CLICK_MODEL is the click model to use for this example (see the list of implemented models below); $SESSION_NUM is the number of search sessions to consider.

Currently, the following click models are implemented and can be used for this example:

• GCTR (global CTR, aka, random click model)
• RCTR (rank-based CTR)
• DCTR (document-based CTR)
• PBM (position-based model)
• CM (cascade model)
• UBM (user browsing model)
• DCM (dependent click model)
• DBN (dynamic Bayesian network)
• SDBN (simplified DBN)

1. Inherit from click_models.ClickModel

    class NewClickModel(ClickModel)
   

2. Define the names of the model parameters:

    param_names = Enum('NCMParamNames', 'one_param another_param')
   

3. Choose appropriate containers for these parameters (see more on this below). Usually, relevance-related parameters depend on a query and a document and so can be stored in QueryDocumentParamContainer. Examination-related parameters usually depend on the document rank and so can be stored in either RankParamContainer or RankSquaredParamContainer. Sometimes, there is a single examination parameter, stored in SingleParamContainer.

4. Choose an appropriate inference method for the click model (see more on this below). If all random variables of the model are observed, use MLEInference. Otherwise, use EMInference. For other options see below.

5. Initialize the click model using the chosen parameter names, containers and inference:

    def __init__(self):
        # Specific containers are used just for the purpose of example
        self.params = {self.param_names.one_param: QueryDocumentParamContainer(),
                       self.param_names.another_param: RankSquaredParamContainer.default()}
        # MLE inference is used just for the purpose of example
        self._inference = MLEInference()
   

6. Implement model parameters. Note that the parameter implementation usually depends on the chosen inference method, so the same model can have different implementations of its parameters for different inference methods. For example, the standard DBN model uses the EM inference, while its simplified version uses the MLE inference. These two versions of DBN need different implementations of the DBN parameters. Thus, it makes sense to name parameter classes as follows: Param. To implement a click model parameter, follow this procedure:

• Inherit from click_models.Param or one of its children. For example, there are predefined classes click_models.ParamEM and click_models.ParamMLE with basic functionality for parameters that implement either EM or MLE inference.

    class NCMParamMLE(ParamMLE)
  

• Implement the update method. This implementation depends on the chosen inference method. For example, in the MLE inference, the values of parameters for a particular search result in a particular search session are calculated based on this search session and the rank of the result. In the EM inference, the values of parameters from the previous iteration are used in addition to the session and rank. For ready-to-use updating formulas of standard click models, please refer to Chapter 4 of our book. Updating formulas for new click models can also be derived based on instructions of Chapter 4.

    Aleksandr Chuklin, Ilya Markov, Maarten de Rijke.
    Click Models for Web Search.
    Morgan & Claypool Publishers, 2015.
  

7. Implement abstract methods inherited from click_models.ClickModel.

• get_session_params: TODO
• get_conditional_click_probs: TODO
• predict_click_probs: TODO

TODO

TODO

• The project is partially funded by the grant P2T1P2_152269 of the Swiss National Science Foundation.
• Initially inspired by the clickmodels project.
• Contributors: Aleksandr Chuklin, Artem Grotov, Luka Stout, Finde Xumara, Bart Vredebregt, Nick de Wolf.