Click chain model in web search

By Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael J. Taylor, Yi Min Wang, Christos Faloutsos. 2009

258 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Information Systems

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In Computer Science Applications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, background): “, [9, 14] and their variants) are likely much larger than usual improvements of click-based metrics in nowadays commercial search engines that are already highly optimized.”
Off-policy evaluation for slate recommendation (2016, background): “Another line of work constructs parametric models of slate quality [8, 16, 14] (see also Sec.”
Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study (2015, background): “Prediction errors of state-of-the-art user click models ( e.g. , [7, 12] and their variants) are likely much larger than usual improvements of click-based metrics in nowadays commercial search engines that are already highly optimized.”
Refining Recency Search Results with User Click Feedback (2011, methodology): “User behavior models ( e.g. , [10, 5, 14]) are developed based on click log data, whose outputs were then used as features for batch-training a ranking function.”
Time-Aware Click Model (2016, methodology): “To address these problems, researchers have proposed several click models to describe users’ practical browsing behavior and to obtain an unbiased estimation of result relevance [Guo et al. 2009a; Dupret and Piwowarski 2008; Chapelle and Zhang 2009].”
Toward Predicting the Outcome of an A/B Experiment for Search Relevance (2015, unknown): “, [8, 10, 11]).”
Click Models for Web Search (2015, methodology): “e dependent click model (DCM) by Guo et al. [2009b] is an extension of the cascade model that is meant to handle sessions with multiple clicks.”
Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Training Heuristic [69], [70], [71], [72], [73], [74], [75], [76] Sampling [30], [43], [74], [77], [78], [79] Exposure-based model [7], [8], [30], [31], [80] Others [5], [6], [81], [82], [83], [84], [85] Position Bias Click models [86], [87], [88], [89], [90], [91], [92] Propensity Score [5], [93], ”
Learning to Rank with Selection Bias in Personal Search (2016, methodology): “Later work also introduced a variety of click modeling techniques including, among many others, a dynamic Bayesian network click model [8], click chain model [17], session utility model [14], whole page click model [9], multiple browsing model [15], and a general click model [36].”
Online Evaluation for Information Retrieval (2016, background): “Similar insights have led to work that focuses on interactions other than clicks, such as touch-based interaction with mobile devices [Guo et al., 2011, 2013a], and extensions of the concept of abandonment to mobile devices [Williams et al., 2016]. Recently introduced personal assistants such as Sir”
Position Bias Estimation for Unbiased Learning to Rank in Personal Search (2018, methodology): “More sophisticated models (e.g., UBM [14], DBN [7], and CCM [17]) build upon these two classic models.”
Cascading Bandits: Learning to Rank in the Cascade Model (2015, methodology): “While these more complex models provide a better ﬁt to actual web data, the cascade model is still attractive as it is simpler and still provides a reasonable ﬁt.”