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

By Georges Dupret, Benjamin Piwowarski. 2008

498 citations recorded by the source corpus.

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In Information Systems

Citations in the corpus

Off-policy evaluation for slate recommendation (2016, background): “Another line of work constructs parametric models of slate quality [8, 16, 14] (see also Sec.”
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, background): “These skipped items influence user’s click/order behaviors [6], which can help us gain better understandings about users’ preferences.”
Leveraging Passage-level Cumulative Gain for Document Ranking (2020, background): “Sogou-QCL [44] is a large-scale public benchmark dataset for document ranking, which consists of 537,366 queries, 5,480,860 documents and various kinds of click model-based relevance labels, such as PSCM [37], UBM [6] and so on.”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “Fortunately this is a common problem encountered in estimating the click-through-rates of search results and search ads (Dupret and Piwowarski 2008),(Chen and Yan 2012), (Craswell et al. 2008).”
A dynamic bayesian network click model for web search ranking (2009, methodology): “We have not done a formal comparison with the model of [ 10 ], but preliminary experiments show that it suers from the same problem as the other position model described above.”
Bias on the web (2018, methodology): “To avoid ranking bias, Web developers need to de-bias click distribution so they can use click data to improve and evaluate ranking algorithms.(11,12) Otherwise, the popular pages become even more popular.”
Interactively optimizing information retrieval systems as a dueling bandits problem (2009, methodology): “Carterette, B., & Jones, R. (2007).”
Click Models for Web Search (2015, methodology): “is model was implicitly used by Dupret and Piwowarski [2008] as a baseline for perplexity evaluation.”
Equity of Attention: Amortizing Individual Fairness in Rankings (2018, result): “The existence of position bias in rankings of search results has been revealed by a number of eye-tracking and other empirical studies [10, 11, 19].”
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.”
Toward Predicting the Outcome of an A/B Experiment for Search Relevance (2015, background): “Much progress has been made in the past that led to reﬁned models that deepen our understanding of user engagement ( e.g. , [8, 10, 11]).”
Big Data or Right Data? (2013, background): “One of the most well-known examples is click data in search engines, where the data is biased by the ranking and the user interface (see for example [10, 7]).”
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): “In order to estimate these marginals, the click models often assume access to large quantities of click data for each document x given the query [8, 11, 15].”
Online Evaluation for Information Retrieval (2016, background): “, 2009a] or a browsing model [Dupret and Piwowarski, 2008].”
Time-Aware Click Model (2016, methodology): “The standard deviation of the TACM at different query frequencies was only 0.03, whereas it was 0.08 for the PSCM and 0.20 for the UBM.”