Category ranking for personalized search

by: Christos Makris, Yannis Panagis, Evangelos Sakkopoulos, Athanasios Tsakalidis

Data & Knowledge Engineering, Vol. 60, No. 1. (January 2007), pp. 109-125.

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Abstract

Despite the effectiveness of search engines, the persistently increasing amount of web data continuously obscures the search task. Efforts have thus concentrated on personalized search that takes account of user preferences. A new concept is introduced towards this direction; search based on ranking of local set of categories that comprise a user search profile. New algorithms are presented that utilize web page categories to personalize search results. Series of user-based experiments show that the proposed solutions are efficient. Finally, we extend the application of our techniques in the design of topic-focused crawlers, which can be considered an alternative personalized search.

@article{citeulike:1109414, abstract = {Despite the effectiveness of search engines, the persistently increasing amount of web data continuously obscures the search task. Efforts have thus concentrated on personalized search that takes account of user preferences. A new concept is introduced towards this direction; search based on ranking of local set of categories that comprise a user search profile. New algorithms are presented that utilize web page categories to personalize search results. Series of user-based experiments show that the proposed solutions are efficient. Finally, we extend the application of our techniques in the design of topic-focused crawlers, which can be considered an alternative personalized search.}, author = {Makris, Christos and Panagis, Yannis and Sakkopoulos, Evangelos and Tsakalidis, Athanasios }, booktitle = {Intelligent Data Mining}, citeulike-article-id = {1109414}, doi = {http://dx.doi.org/10.1016/j.datak.2005.11.006}, journal = {Data \& Knowledge Engineering}, keywords = {adaptive-search, adaptive-web, www-search}, month = {January}, number = {1}, pages = {109--125}, posted-at = {2007-05-12 05:47:37}, priority = {2}, title = {Category ranking for personalized search}, url = {http://dx.doi.org/10.1016/j.datak.2005.11.006}, volume = {60}, year = {2007} }

RIS record

TY - JOUR ID - citeulike:1109414 L3 - citeulike-article-id:1109414 N2 - Despite the effectiveness of search engines, the persistently increasing amount of web data continuously obscures the search task. Efforts have thus concentrated on personalized search that takes account of user preferences. A new concept is introduced towards this direction; search based on ranking of local set of categories that comprise a user search profile. New algorithms are presented that utilize web page categories to personalize search results. Series of user-based experiments show that the proposed solutions are efficient. Finally, we extend the application of our techniques in the design of topic-focused crawlers, which can be considered an alternative personalized search. IS - 1 JF - Data & Knowledge Engineering EP - 125 TI - Category ranking for personalized search VL - 60 T2 - Intelligent Data Mining SP - 109 KW - adaptive-search KW - adaptive-web KW - www-search AU - Makris, Christos AU - Panagis, Yannis AU - Sakkopoulos, Evangelos AU - Tsakalidis, Athanasios PY - 2007/01// UR - http://dx.doi.org/10.1016/j.datak.2005.11.006 ER -

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