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Classification and Recommendation With Data Streams
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Author(s): Bruno Veloso (INESC TEC, Portugal & University Portucalense, Portugal), João Gama (INESC TEC, Portugal & FEP, University of Porto, Portugal)and Benedita Malheiro (Polytechnic Institute of Porto, Portugal & INESC TEC, Portugal)
Copyright: 2021
Pages: 10
Source title:
Encyclopedia of Information Science and Technology, Fifth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3479-3.ch047
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Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.
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