The thesis introduces the reputation-based ranking of Web information sources and compares it with the Google’s ranking. Moreover, it determines the relevance of the reputation metrics with respect to the Google's ranking algorithm. In the work, we focused on the blogs and forums since they allow users to share their opinions and insert their comments about the topics and assessing reputation for them is a crucial element. The data quality literature defines reputation as a dimension of information quality that measures the trustworthiness and importance of an information source. Reputation is recognized as a multidimensional quality attribute. The variables that affect the overall reputation of an information source are related to the institutional clout of the source, to the relevance of the source in a given context, and to the general quality of the source’s information content. A set of metrics measuring the reputation of Web information sources has been defined. These metrics have been empirically assessed for the top 15 sources identified by Google as a response to ten queries in the tourism domain especially in New-York and London. Then, we have compared Google’s ranking with the reputation-based ranking for all the ten queries using different kinds of analysis. Results show that there is a difference (distance) between the Google's ranking and the ranking that is based on the reputation metrics. Moreover, the reputation metrics have different relevance to Google ranking algorithm since each ranking that is based along each of the reputation metrics has different distance values when comparing them with the Google's ranking. At the next step the whole process is implemented as a web service. Our main focus is in the areas of application implementation and enhancement, process optimization, interfaces and project management. We have finally published our project over internet where you can access it on the following URL: www.ritrovatore.com
Implementation of reputation based selection of web information sources
SAEEDI, HAMIDREZA;SOJOUDI, MOHSEN
2010/2011
Abstract
The thesis introduces the reputation-based ranking of Web information sources and compares it with the Google’s ranking. Moreover, it determines the relevance of the reputation metrics with respect to the Google's ranking algorithm. In the work, we focused on the blogs and forums since they allow users to share their opinions and insert their comments about the topics and assessing reputation for them is a crucial element. The data quality literature defines reputation as a dimension of information quality that measures the trustworthiness and importance of an information source. Reputation is recognized as a multidimensional quality attribute. The variables that affect the overall reputation of an information source are related to the institutional clout of the source, to the relevance of the source in a given context, and to the general quality of the source’s information content. A set of metrics measuring the reputation of Web information sources has been defined. These metrics have been empirically assessed for the top 15 sources identified by Google as a response to ten queries in the tourism domain especially in New-York and London. Then, we have compared Google’s ranking with the reputation-based ranking for all the ten queries using different kinds of analysis. Results show that there is a difference (distance) between the Google's ranking and the ranking that is based on the reputation metrics. Moreover, the reputation metrics have different relevance to Google ranking algorithm since each ranking that is based along each of the reputation metrics has different distance values when comparing them with the Google's ranking. At the next step the whole process is implemented as a web service. Our main focus is in the areas of application implementation and enhancement, process optimization, interfaces and project management. We have finally published our project over internet where you can access it on the following URL: www.ritrovatore.comFile | Dimensione | Formato | |
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Final-Thesis.pdf
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Descrizione: IMPLEMENTATION OF REPUTATION BASED SELECTION OF WEB INFORMATION SOURCES
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https://hdl.handle.net/10589/23521