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Trust network datasets - TrustLet
a free, collaborative project for collecting and analyzing information about trust metrics.
Recommender Systems for Social Bookmarking
In this thesis, we investigate how recommender systems can be applied to the domain of social bookmarking. More specifically, we want to investigate the task of item recommendation. For this purpose, interesting and relevant items---bookmarks or scientific articles---are retrieved and recommended to the user. Recommendations can be based on a variety of information sources about the user and the items. It is a difficult task as we are trying to predict which items out of a very large pool would be relevant given a user's interests, as represented by the items which the user has added in the past. In our experiments we distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. We are among the first to investigate this content-based aspect of recommendation for social bookmarking websites. We compare and combine the content-based aspect with the more common usage-based approaches.
SPEAR Algorithm - Michael G. Noll
Telling Experts from Spammers: Expertise Ranking in Folksonomies"
Technology Review: A Better Way to Rank Expertise Online
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the fact is [that] quantity does not imply quality.
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The new algorithm is called Spamming-resistant Expertise Analysis and Ranking (SPEAR) and is based on the well-known information-retrieval algorithm called HITS that is used by search engines like Google to rank Web pages.
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Michael Nielsen » The Google Technology Stack
Part of what makes Google such an amazing engine of innovation is their internal technology stack: a set of powerful proprietary technologies that makes it easy for Google developers to generate and process enormous quantities of data. According to a senior Microsoft developer who moved to Google, Googlers work and think at a higher level of abstraction than do developers at many other companies, including Microsoft: “Google uses Bayesian filtering the way Microsoft uses the if statement” (Credit: Joel Spolsky). This series of posts describes some of the technologies that make this high level of abstraction possible.
Data Miners Blog: Data Mining and Statistics
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The way I think about it, data mining is the process of using data to figure stuff out.
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There is, however, a cultural difference between people who call themselves statisticians and people who call themselves data miners. This difference has its origins in different expectations about data size.
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