Domain-specific queries and Web search personalization: some investigations
Autor: | Rocco De Nicola, Angelo Spognardi, Marinella Petrocchi, Francesco Tiezzi, Van Tien Hoang |
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Rok vydání: | 2015 |
Předmět: |
FOS: Computer and information sciences
Matching (statistics) Information retrieval Web search query software Computer science Process (engineering) lcsh:Mathematics Information Bubbles lcsh:QA1-939 Filter (software) lcsh:QA75.5-76.95 Computer Science - Information Retrieval Web Search Personalization Personalization Domain (software engineering) Search engine Selection (linguistics) lcsh:Electronic computers. Computer science MIB My Information Bubble project Information Retrieval (cs.IR) |
Zdroj: | WWV 11th International Workshop on Automated Specification and Verification of Web Systems (WWV'15)., pp. 51–58, Oslo, Norvegia, 23/06/2015 info:cnr-pdr/source/autori:A. Spognardi, M. Petrocchi, R. De Nicola, V. T. Hoang, F. Tiezzi/congresso_nome:11th International Workshop on Automated Specification and Verification of Web Systems (WWV'15)./congresso_luogo:Oslo, Norvegia/congresso_data:23%2F06%2F2015/anno:2015/pagina_da:51/pagina_a:58/intervallo_pagine:51–58 Electronic Proceedings in Theoretical Computer Science, Vol 188, Iss Proc. WWV 2015, Pp 51-58 (2015) |
ISSN: | 2075-2180 |
DOI: | 10.4204/eptcs.188.6 |
Popis: | Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their preferences. This paper moves from early results on quantification of personalization over Google search query results. Inspired by previous works, we have carried out some experiments consisting of search queries performed by a battery of Google accounts with differently prepared profiles. Matching query results, we quantify the level of personalization, according to topics of the queries and the profile of the accounts. This work reports initial results and it is a first step a for more extensive investigation to measure Web search personalization. In Proceedings WWV 2015, arXiv:1508.03389 |
Databáze: | OpenAIRE |
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