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Modeling clicks using document popularity

Published: 04 April 2016 Publication History

Abstract

It is commonly believed, that implicit user feedback such as clicks from search logs, can be a strong indicator of user preferences during web search. Moreover, proper click log data analysis can lead to accurate document relevance estimation. As a result, an effective approach to properly interpret users' search behaviour was the development of user click models, designed to reduce position bias and produce more attractive users' search needs.
Here, in this paper we present a novel click model based on a Bayesian network, for estimating document relevance from click-through data. In contrast with previous works, our model focuses only on the estimation of actual document relevance and not on perceived relevance, which we take to be inferred directly from document popularity. We design the model to be scalable and incremental according to modern computational needs and experiments show that our proposed model achieves better scores in log-likelihood and predicts relevance more accurately than current state-of-the-art.

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cover image ACM Conferences
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
April 2016
2360 pages
ISBN:9781450337397
DOI:10.1145/2851613
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 April 2016

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Author Tags

  1. click models
  2. evaluation
  3. ranking
  4. web search

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  • Research-article

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  • ICT4Growth

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SAC 2016
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SAC 2016: Symposium on Applied Computing
April 4 - 8, 2016
Pisa, Italy

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SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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