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Discovering User Behavioral Features to Enhance Information Search on Big Data

Discovering User Behavioral Features to Enhance Information Search on Big Data Discovering User Behavioral Features to Enhance Information Search on Big Data NUNZIATO CASSAVIA and ELIO MASCIARI, ICAR CNR CHIARA PULICE and DOMENICO SACCÀ, DIMES UNICAL Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a user ™s quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Discovering User Behavioral Features to Enhance Information Search on Big Data

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References (37)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2856059
Publisher site
See Article on Publisher Site

Abstract

Discovering User Behavioral Features to Enhance Information Search on Big Data NUNZIATO CASSAVIA and ELIO MASCIARI, ICAR CNR CHIARA PULICE and DOMENICO SACCÀ, DIMES UNICAL Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a user ™s quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Jul 29, 2017

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