• Corpus ID: 12961209

Click Models for Web Search Authors ’ version *

@inproceedings{Chuklin2015ClickMF,
  title={Click Models for Web Search Authors ’ version *},
  author={Aleksandr Chuklin and Ilya Markov},
  year={2015}
}
clickmodels.weebly.com

98 References

Inferring search behaviors using partially observable markov model with duration (POMD)

Partially Observable Markov model with Duration (POMD), a statistical method that addresses the challenge of understanding sophisticated user behaviors from the search log in which some user actions cannot be observed and recorded, is presented.

A user browsing model to predict search engine click data from past observations.

It is confirmed that a user almost always see the document directly after a clicked document, and why documents situated just after a very relevant document are clicked more often is explained.

An experimental comparison of click position-bias models

A cascade model, where users view results from top to bottom and leave as soon as they see a worthwhile document, is the best explanation for position bias in early ranks.

Expected reciprocal rank for graded relevance

This work presents a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents and calls it Expected Reciprocal Rank (ERR).

BBM: bayesian browsing model from petabyte-scale data

This paper proposes the Bayesian Browsing Model (BBM), a new modeling technique with following advantages: (a) it does exact inference; (b) it is single-pass and parallelizable; (c)It is effective.

A dynamic bayesian network click model for web search ranking

A Dynamic Bayesian Network is proposed which aims at providing us with unbiased estimation of the relevance from the click logs and shows that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.

Probabilistic Graphical Models - Principles and Techniques

The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

Incorporating vertical results into search click models

Experimental results show that the new Vertical-aware Click Model (VCM) is better at interpreting user click behavior on federated searches in terms of both log-likelihood and perplexity than existing models.

Beyond ten blue links: enabling user click modeling in federated web search

The proposed novel federated click model (FCM) can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood.

User-click modeling for understanding and predicting search-behavior

This work identifies and considers two new biases in TCM that indicate that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer, and proposes a task-centric click model~(TCM), which characterizes user behavior related to a task as a collective whole.
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