Search

Your search keyword '"Dupret, Georges"' showing total 49 results
Start Over Author "Dupret, Georges"
49 results on '"Dupret, Georges"'

1. ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics

Author

Dupret, Georges, Sozinov, Konstantin, Gonzalez, Carmen Barcena, Zacks, Ziggy, Yuan, Amber, Carterette, Benjamin, Mai, Manuel, Bansal, Shubham, Liang, Gwo, Lien, Gatash, Andrey, Ojeda, Roberto Sanchis, and Lalmas, Mounia

Subjects
Computer Science - Computational Engineering, Finance, and Science, Statistics - Applications
Abstract

Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key to informing product decisions. Online controlled experiments by A/B testing may provide the best data to support such decisions with high confidence, but can be time-consuming and expensive, especially when one wants to understand impact to key business metrics such as retention or long-term value. Offline experimentation allows one to rapidly iterate and test, but often cannot provide the same level of confidence, and cannot easily shine a light on impact on business metrics. We introduce a novel, lightweight, and flexible approach to investigating hypotheses, called scenario analysis, that aims to support product leaders' decisions using data about users and estimates of business metrics. Its strengths are that it can provide guidance on trade-offs that are incurred by growing or shifting consumption, estimate trends in long-term outcomes like retention and other important business metrics, and can generate hypotheses about relationships between metrics at scale.

Published
2024

2. Beyond Cumulated Gain and Average Precision: Including Willingness and Expectation in the User Model

Author

Piwowarski, Benjamin, Dupret, Georges, and Lalmas, Mounia

Subjects
Computer Science - Information Retrieval, H.3.3, H.3.4
Abstract

In this paper, we define a new metric family based on two concepts: The definition of the stopping criterion and the notion of satisfaction, where the former depends on the willingness and expectation of a user exploring search results. Both concepts have been discussed so far in the IR literature, but we argue in this paper that defining a proper single valued metric depends on merging them into a single conceptual framework.

Published
2012

3. Learning to Rank Query Recommendations by Semantic Similarities

Author

Fujita, Sumio, Dupret, Georges, and Baeza-Yates, Ricardo

Subjects
Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval, H.3.3, H.3.5
Abstract

Logs of the interactions with a search engine show that users often reformulate their queries. Examining these reformulations shows that recommendations that precise the focus of a query are helpful, like those based on expansions of the original queries. But it also shows that queries that express some topical shift with respect to the original query can help user access more rapidly the information they need. We propose a method to identify from the query logs of past users queries that either focus or shift the initial query topic. This method combines various click-based, topic-based and session based ranking strategies and uses supervised learning in order to maximize the semantic similarities between the query and the recommendations, while at the same diversifying them. We evaluate our method using the query/click logs of a Japanese web search engine and we show that the combination of the three methods proposed is significantly better than any of them taken individually., Comment: 2nd International Workshop on Usage Analysis and the Web of Data (USEWOD2012) in the 21st International World Wide Web Conference (WWW2012), Lyon, France, April 17th, 2012

Published
2012

4. Models of User Engagement

Author

Lehmann, Janette, Lalmas, Mounia, Yom-Tov, Elad, Dupret, Georges, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Masthoff, Judith, editor, Mobasher, Bamshad, editor, Desmarais, Michel C., editor, and Nkambou, Roger, editor

Published
2012
Full Text
View/download PDF

5. Discounted Cumulative Gain and User Decision Models

Author

Dupret, Georges, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Grossi, Roberto, editor, Sebastiani, Fabrizio, editor, and Silvestri, Fabrizio, editor

Published
2011
Full Text
View/download PDF

6. Using Related Queries to Improve Web Search Results Ranking

Author

Dupret, Georges, Zilleruelo-Ramos, Ricardo, Fujita, Sumio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Chavez, Edgar, editor, and Lonardi, Stefano, editor

Published
2010
Full Text
View/download PDF

7. Enhancing Educational-Material Retrieval Using Authored-Lesson Metadata

Author

Motelet, Olivier, Piwowarski, Benjamin, Dupret, Georges, Pino, Jose A., Baloian, Nelson, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ziviani, Nivio, editor, and Baeza-Yates, Ricardo, editor

Published
2007
Full Text
View/download PDF

9. Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis

Author

Bast, Holger, Dupret, Georges, Majumdar, Debapriyo, Piwowarski, Benjamin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ackermann, Markus, editor, Berendt, Bettina, editor, Grobelnik, Marko, editor, Hotho, Andreas, editor, Mladenič, Dunja, editor, Semeraro, Giovanni, editor, Spiliopoulou, Myra, editor, Stumme, Gerd, editor, Svátek, Vojtěch, editor, and van Someren, Maarten, editor

Published
2006
Full Text
View/download PDF

10. A Statistical Model of Query Log Generation

Author

Dupret, Georges, Piwowarski, Benjamin, Hurtado, Carlos, Mendoza, Marcelo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Crestani, Fabio, editor, Ferragina, Paolo, editor, and Sanderson, Mark, editor

Published
2006
Full Text
View/download PDF

11. Principal Components for Automatic Term Hierarchy Building

Author

Dupret, Georges, Piwowarski, Benjamin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Crestani, Fabio, editor, Ferragina, Paolo, editor, and Sanderson, Mark, editor

Published
2006
Full Text
View/download PDF

12. Recommending Better Queries from Click-Through Data

Author

Dupret, Georges, Mendoza, Marcelo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Consens, Mariano, editor, and Navarro, Gonzalo, editor

Published
2005
Full Text
View/download PDF

27. Bootstrap re-sampling and cross-validation for neural network learning

Author

Dupret, Georges, Koda, Masato, 香田, 正人, コウダ, マサト, Dupret, Georges, Koda, Masato, 香田, 正人, and コウダ, マサト

Abstract

Includes bibliographic references, A technical framework to assess the impact of re-sampling on the ability of a neural network is presented to correctly learn a classification problem.We use the bootstrap expression of the prediction error to identify the optimal re-sampling proportions in a numerical experiment with binary classes and propose a new,simple method to estimate this optimal proportion.An upper and a lower bounds for the optimal proportion are derived based on Bayes decision rule.The analytical considerations to extend the present method to crossvalidation are also illustrated., http://www.tulips.tsukuba.ac.jp/limedio/dlam/B14/B1457845/1.pdf

Published
2011

28. Measuring inter-site engagement

Author

Yom-Tov, Elad, primary, Lalmas, Mounia, additional, Baeza-Yates, Ricardo, additional, Dupret, Georges, additional, Lehmann, Janette, additional, and Donmez, Pinar, additional

Published
2013
Full Text
View/download PDF

29. WSCD2013

Author

Serdyukov, Pavel, primary, Dupret, Georges, additional, and Craswell, Nick, additional

Published
2013
Full Text
View/download PDF

35. WSCD 2012

Author

Serdyukov, Pavel, primary, Craswell, Nick, additional, and Dupret, Georges, additional

Published
2012
Full Text
View/download PDF

44. Enhancing Educational-Material Retrieval Using Authored-Lesson Metadata.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Ziviani, Nivio, Baeza-Yates, Ricardo, Motelet, Olivier, Piwowarski, Benjamin, and Dupret, Georges

Abstract

Many authors believe that in order to achieve coherence and flexibility at the same time in multimedia-based learning units, it is highly recommendable to structure the different components as a graph. In a lesson graph, educational resources are encapsulated into learning objects (LO) along with their respective metadata and are interconnected through different kind of rhetorical and semantical relationships. The LOs of these graphs are stored within repositories, where their metadata are used to ease their retrieval. In this paper we propose to integrate the processes of searching LOs and editing the lesson graph. This new framework extends traditional keyword and metadata search to take advantage of the information stored implicitly in the lesson graph structure, making LOs retrieval more effective and the expression of queries more intuitive. The retrieval of the learning material consists of two processes: (1) The user first defines the topological location of a required piece of educational material within the lesson graph, this is, its relationships with other pieces. (2) Then, the user issues a traditional keyword query, which is processed by an IR system modified to take the graph structure into account. Experiments show the advantages of this approach. [ABSTRACT FROM AUTHOR]

Published
2007
Full Text
View/download PDF

45. Automatic Query Recommendation using Click-Through Data.

Author

Debenham, John, Dupret, Georges, and Mendoza, Marcelo

Abstract

We present a method to help a user redefine a query suggesting a list of similar queries. The method proposed is based on click-through data were sets of similar queries could be identified. Scientific literature shows that similar queries are useful for the identification of different information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identification of better queries is useful for query disambiguation and query specialization. [ABSTRACT FROM AUTHOR]

Published
2006
Full Text
View/download PDF

46. A Statistical Model of Query Log Generation.

Author

Crestani, Fabio, Ferragina, Paolo, Sanderson, Mark, Dupret, Georges, Piwowarski, Benjamin, Hurtado, Carlos, and Mendoza, Marcelo

Abstract

Query logs record past query sessions across a time span. A statistical model is proposed to explain the log generation process. Within a search engine list of results, the model explains the document selection - a user's click - by taking into account both a document position and its popularity. We show that it is possible to quantify this influence and consequently estimate document "un-biased" popularities. Among other applications, this allows to re-order the result list to match more closely user preferences and to use the logs as a feedback to improve search engines. [ABSTRACT FROM AUTHOR]

Published
2006
Full Text
View/download PDF

47. Principal Components for Automatic Term Hierarchy Building.

Author

Crestani, Fabio, Ferragina, Paolo, Sanderson, Mark, Dupret, Georges, and Piwowarski, Benjamin

Abstract

We show that the singular value decomposition of a term similarity matrix induces a term hierarchy. This decomposition, used in Latent Semantic Analysis and Principal Component Analysis for text, aims at identifying "concepts" that can be used in place of the terms appearing in the documents. Unlike terms, concepts are by construction uncorrelated and hence are less sensitive to the particular vocabulary used in documents. In this work, we explore the relation between terms and concepts and show that for each term there exists a latent subspace dimension for which the term coincides with a concept. By varying the number of dimensions, terms similar but more specific than the concept can be identified, leading to a term hierarchy. Keywords: Term hierarchy, principal component analysis, latent semantic analysis, information retrieval. [ABSTRACT FROM AUTHOR]

Published
2006
Full Text
View/download PDF

48. Recommending Better Queries from Click-Through Data.

Author

Consens, Mariano, Navarro, Gonzalo, Dupret, Georges, and Mendoza, Marcelo

Abstract

We present a method to help a user redefine a query based on past users experience, namely the click-through data as recorded by a search engine. Unlike most previous works, the method we propose attempts to recommend better queries rather than related queries. It is effective at identifying query specialization or sub-topics because it take into account the co-occurrence of documents in individual query sessions. It is also particularly simple to implement. [ABSTRACT FROM AUTHOR]

Published
2005
Full Text
View/download PDF

49. Evaluation in (XML) Information Retrieval: Expected Precision-Recall with User Modelling (EPRUM).

Author

Piwowarski, Benjamin and Dupret, Georges

Subjects
INFORMATION storage & retrieval systems, XML (Extensible Markup Language), INFORMATION retrieval, DOCUMENTATION, INFORMATION science
Abstract

Standard Information Retrieval (IR) metrics assume a simple model where documents are understood as independent units. Such an assumption is not adapted to new paradigms like XML or Web IR where retrievable informations are parts of documents or sets of related documents. Moreover, classical hypotheses assumes that the user ignores the structural or logical context of document elements and hence the possibility of navigation between units. EPRUM is a generalisation of Precision-Recall (PR) that aims at allowing the user to navigate or browse in the corpus structure. Like the Cumulated Gain metrics, it is able to handle continuous valued relevance. We apply and compare EPRUM in the context of XML Retrieval -- a very active field for evaluation metrics. We also explain how EPRUM can be used in other IR paradigms. [ABSTRACT FROM AUTHOR]

Published
2006

Catalog

Books, media, physical & digital resources
See catalog results