Faderank: An Incremental Algorithm for Ranking Twitter Users
| Autor: | Stefano Lande, Alessandro Massa, Massimo Bartoletti |
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| Rok vydání: | 2016 |
| Předmět: |
Process (engineering)
Computer science media_common.quotation_subject Context (language use) 02 engineering and technology computer.software_genre Ranking (information retrieval) Constant (computer programming) Time windows 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Learning to rank Incremental algorithm Data mining computer Reputation media_common |
| Zdroj: | Web Information Systems Engineering – WISE 2016 ISBN: 9783319487427 WISE (2) |
| DOI: | 10.1007/978-3-319-48743-4_5 |
| Popis: | User reputation is a crucial indicator in social networks, where it is exploited to promote authoritative content and to marginalize spammers. To be accurate, reputation must be updated periodically, taking into account the whole historical data of user activity. In big social networks like Twitter and Facebook, these updates would require to process a huge amount of historical data, and therefore pose serious performance issues. We address these issues in the context of Twitter, by studying a technique which can update user reputation in constant time. This is obtained by using an arbitrary ranking algorithm to compute user reputation in the most recent time window, and by combining it with a summary of historical data. Experimental evaluation on large datasets show that our technique improves the performance of existing ranking algorithms, at the cost of a negligible degradation of their precision. |
| Databáze: | OpenAIRE |
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