Proceedings Article10.1145/1458502.1458523
Boosting the ranking function learning process using clustering
Giorgos Giannopoulos,Theodore Dalamagas,Magdalini Eirinaki,Timos Sellis +3 more
- 30 Oct 2008
- pp 125-132
TL;DR: This work presents a method that overcomes the issue of personalizing the ranked result list based on user feedback by exploiting all search results, both rated and unrated, in order to train a ranking function.
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Abstract: As the Web continuously grows, the results returned by search engines are too many to review. Lately, the problem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present a method that overcomes this issue by exploiting all search results, both rated and unrated, in order to train a ranking function. Given a small initial set of user feedback for some search results, we first perform clustering on all results returned by the search. Based on the clusters created, we extend the initial set of rated results, including new, unrated results. Then, we use a popular training method (Ranking SVM) to train a ranking function using the expanded set of results. The experiments show that our method approximates sufficiently the results of an "ideal" system where all results of each query should be rated in order to be used as training data, something that is not feasible in a real-world scenario.
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Citations
Learning Bregman Distance Functions for Semi-Supervised Clustering
TL;DR: This paper aims to learn a Bregman distance function using a nonparametric approach that is similar to Support Vector Machines, and verifies the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering.
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Dominant user context (DUC) filtering framework for web personalized search
Nazihah Abdul Kadir,Anitawati Mohd Lokman,Aishah Ahmad +2 more
- 06 Dec 2012
TL;DR: The work reported in this paper attempt to design Dominant User Context (DUC) Filtering Framework for personalized search and enables the enhancement of personalized search result by matching the user behavior, interest and ontology of metadata using the search keyword.
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An improved fuzzy system for representing web pages in clustering tasks
Alberto Pérez García-Plaza
- 30 Jan 2013
TL;DR: An improved fuzzy system for representing web pages in clustering is presented in this article, with the goal of aprovechar al maximo un modelo borroso de representacion of documentos HTML for problemas de clustering.
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