No description, website, or topics provided.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
bin
relevance_test_dataset
sample
user_preference_test_dataset
README.md

README.md

PSCMModel

PSCMModel is a small set of Python scripts for the user click models based on Yandex version (https://github.com/varepsilon/clickmodels).

A Click Model is a probabilistic graphical model used to predict search engine click data from past observations.

This project is aimed to implement recently proposed click models and intended to be easy-to-read and easy-to-modify. If it's not, please let me know how to improve it :)

Models Implemented

  • Partially Sequential Click Model ( PSCM ) model: Chao Wang, Yiqun Liu, Meng Wang, Ke Zhou, Jian-Yun Nie, Shaoping Ma. Incorporating Non-sequential Behavior into Click Models. SIGIR (2015).
  • Temporal Hidden Click Model ( THCM ) model: Danqing Xu, Yiqun Liu, Min Zhang, Shaoping Ma. Incorporating revisiting behaviors into click models. WSDM (2012).
  • Temporal Click Model ( TCM ) model: Wanhong Xu, Eren Manavoglu, Erick Cantú-Paz. Temporal Click Model for Sponsored Search. SIFIR (2010).
  • Partially Observable Markov Model ( POM ) model: Kuansan Wang, Nikolas Gloy, Xiaolong Li. Inferring search behaviors using partially observable markov (pom) model. WSDM (2010).
  • Dynamic Bayesian Network ( DBN ) model: Chapelle, O. and Zhang, Y. 2009. A dynamic bayesian network click model for web search ranking. WWW (2009). (This model is exactly the same implementation as Yandex version)
  • User Browsing Model ( UBM ): Dupret, G. and Piwowarski, B. 2008. A user browsing model to predict search engine click data from past observations. SIGIR (2008).

Files

README.md

This file.

bin/

Directory with the scripts.

sample/

Directory with the sample dataset.

relevance_test_dataset/

query-result relevance data for PSCM model's paper (Section 5.3)

user_preference_test_dataset/

user preference test data for PSCM model's paper (Section 5.4)

Format of the Input Data

A small example can be found under sample/ (tab-separated). 5 files are included in this directory:

  • query_id: encode each query into a unique id.
    • e.g.: "test 1 10 5" means query ("test") with a unique id (1), 10 sessions are found in search logs and 5 sessions contain click action.
  • query_class: The probability of been each searh intent for each query.
    • e.g.: "test 0.25 0.25 0.25 0.25" means query ("test") has four search intents. Set "query_id 1" for each query if this information is needless.
  • url_id: encode each URL into a unique id.
  • train_data, test_data: search logs, in which each line represents one query-session. 10 tab-separated part are included. The inner separator for each [] is space (" "):
    • query_id [url_id * 10] [click * 10] [click_time * 10] [mouse_feature_1 * 10] [mouse_feature_2 * 10] [mouse_feature_3 * 10] [mouse_feature_4 * 10] [mouse_feature_5 * 10] [mouse_feature_6 * 10]
    • click: 1 represents click, 0 represents no click
    • click_time: >0 represents click time in seconds, -1 represents no click
    • mouse_feature_1: The most left position mouse ever reach to in the result’s display area
    • mouse_feature_2: User’s total right towards movement length in the result’s display area
    • mouse_feature_3: The total dwell time that cursor spends in the result's display area neglecting its horizontal coordinate
    • mouse_feature_4: The total dwell time that cursor spends in the result's display area
    • mouse_feature_5: The total time that cursor hovers over the result's display area
    • mouse_feature_6: The amount of cursor actions (scroll, test select, move times) that appear in the result's display area
    • Ps: Just set mouse feature as 0 if your search logs do not contain mouse movement information.

Usage

in bin/config_sample.py: select click models (e.g.: TEST_MODELS = ['PSCM', 'UBM', 'DBN', 'POM', 'TCM', 'THCM'])

in bin/ : python wc_click_model_inference_by_id.py ../sample

Output

in target data directory, "/output" directory will be automatically generated, in which model results will be logged:

  • model_name.model: Parameters of this model generated from train_data
  • model_name.model.perplexity: Perplexity metrics of this model tested on test_data
  • model_name.model.relevance: Query-URL-Relevance generated from this model