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

Dupret, Georges; Piwowarski, Benjamin

doi:10.1145/1390334.1390392

Cited by 347 publications

(270 citation statements)

References 10 publications

Supporting

Mentioning

267

Contrasting

Unclassified

Order By: Relevance

“…The main challenge is to account for the fact that not all possible rankings are contained in logs. Possible solutions could be based on click models estimated from such data, like the one underlying the click model used in this paper [5,7].…”

Section: Resultsmentioning

confidence: 99%

Balancing Exploration and Exploitation in Learning to Rank Online

Hofmann

Whiteson

Rijke

2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has already been learned to produce results that are acceptable to users. We formulate this challenge as an exploration-exploitation dilemma and present the first online learning to rank algorithm that works with implicit feedback and balances exploration and exploitation. We leverage existing learning to rank data sets and recently developed click models to evaluate the proposed algorithm. Our results show that finding a balance between exploration and exploitation can substantially improve online retrieval performance, bringing us one step closer to making online learning to rank work in practice.

show abstract

Section: Resultsmentioning

confidence: 99%

Balancing Exploration and Exploitation in Learning to Rank Online

Hofmann

Whiteson

Rijke

2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Given a large query click log [26], click models provide a principled approach to inferring user-perceived relevance of web documents. There are click models as the click chain model [16] based on a solid Bayesian framework, the ImpressionRanks [5], the contextual advertising [25,43], the DBN [10], UBM [13], BBM [27] and GCM [49]. Click models provide a principled way of integrating knowledge of user search behaviors to infer user-perceived relevance of web documents.…”

Section: Related Workmentioning

confidence: 99%

Semantic Search on Applicant Tracking System

Ha¹,

Rahman²

2017

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

Web search engines often federate many user queries to relevant structured databases. For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills. The relevant structured data items are then returned to the user along with web search results. Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query. Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills. There are on average 238 keywords per document. In fact, per minute there can be up to 200,000 transactions within all these databases. Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query "Jet fuel Thermal Oxidation" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking. In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models. Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds. Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph. We designed a combination of semantic search that look for web pages per search and database search.

show abstract

“…Following Joachims et al [7], who conducted eye-tracking experiments, there was a series of papers that model user behavior using probabilistic graphical models. The most influential works in this area include the UBM model by Dupret and Piwowarski [6], the Cascade Model by Craswell et al [5] and the DBN model by Chapelle and Zhang [3].…”

Section: Click Modelsmentioning

confidence: 99%

“…As a basic model to modify we use the UBM click model by Dupret and Piwowarski [6]. However, our extensions can equally well be applied to other click models.…”

Section: Click Modelsmentioning

confidence: 99%

Using Intent Information to Model User Behavior in Diversified Search

Chuklin

Serdyukov

Rijke

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A result page of a modern commercial search engine often contains documents of different types targeted to satisfy different user intents (news, blogs, multimedia). When evaluating system performance and making design decisions we need to better understand user behavior on such result pages. To address this problem various click models have previously been proposed. In this paper we focus on result pages containing fresh results and propose a way to model user intent distribution and bias due to different document presentation types. To the best of our knowledge this is the first work that successfully uses intent and layout information to improve existing click models.

show abstract

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

Cited by 347 publications

References 10 publications

Balancing Exploration and Exploitation in Learning to Rank Online

Balancing Exploration and Exploitation in Learning to Rank Online

Semantic Search on Applicant Tracking System

Using Intent Information to Model User Behavior in Diversified Search

Contact Info

Product

Resources

About