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Discovering User Behavioral Features to Enhance Information Search on Big Data

Authors:

Nunziato Cassavia,

Domenico SaccàAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 7, Issue 2

Article No.: 7, Pages 1 - 33

https://doi.org/10.1145/2856059

Published: 29 July 2017 Publication History

Abstract

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. Such features are the result of data exchange activity (called data posting) that enriches information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users. The approach is tested in an important application scenario such as tourist recommendation, but it can be profitably exploited in several other contexts, for example, viral marketing and food education.

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Cited By

Goswami ASinha AGoswami MKumar P(2024)Explicating the mapping between big data and knowledge management: a systematic literature review and future directionsBenchmarking: An International Journal10.1108/BIJ-09-2022-0550Online publication date: 29-Mar-2024
https://doi.org/10.1108/BIJ-09-2022-0550
Yue S(2024)A Multimodal conceptual framework to achieve automated software evolution for context-rich intelligent applicationsInnovations in Systems and Software Engineering10.1007/s11334-024-00591-0Online publication date: 10-Nov-2024
https://doi.org/10.1007/s11334-024-00591-0
Sakas DKamperos ITerzi MKriemadis A(2024)The COVID-19 Crisis Effect on Railways’ Digital Branding: Risk Management Applications Utilizing Big DataComputational and Strategic Business Modelling10.1007/978-3-031-41371-1_6(57-67)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-41371-1_6
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Index Terms

Discovering User Behavioral Features to Enhance Information Search on Big Data
1. Information systems
  1. Data management systems
    1. Information integration
      1. Extraction, transformation and loading

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 7, Issue 2

June 2017

87 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3129288

Editor:
Michelle Zhou
Juji, Inc., USA

Issue’s Table of Contents

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2017

Accepted: 01 June 2016

Revised: 01 June 2016

Received: 01 August 2015

Published in TIIS Volume 7, Issue 2

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Funding Sources

MIUR (the Italian Ministry for Research and University)
PON (national operative program) Project PON04a2_D “INMOTO: INformation and MObility for TOurism,”
Regional Operating Program (POR)

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Cited By

Goswami ASinha AGoswami MKumar P(2024)Explicating the mapping between big data and knowledge management: a systematic literature review and future directionsBenchmarking: An International Journal10.1108/BIJ-09-2022-0550Online publication date: 29-Mar-2024
https://doi.org/10.1108/BIJ-09-2022-0550
Yue S(2024)A Multimodal conceptual framework to achieve automated software evolution for context-rich intelligent applicationsInnovations in Systems and Software Engineering10.1007/s11334-024-00591-0Online publication date: 10-Nov-2024
https://doi.org/10.1007/s11334-024-00591-0
Sakas DKamperos ITerzi MKriemadis A(2024)The COVID-19 Crisis Effect on Railways’ Digital Branding: Risk Management Applications Utilizing Big DataComputational and Strategic Business Modelling10.1007/978-3-031-41371-1_6(57-67)Online publication date: 14-Feb-2024
https://doi.org/10.1007/978-3-031-41371-1_6
Bonifazi GCorradini EUrsino DVirgili L(2023)Modeling, Evaluating, and Applying the eWoM Power of Reddit PostsBig Data and Cognitive Computing10.3390/bdcc70100477:1(47)Online publication date: 9-Mar-2023
https://doi.org/10.3390/bdcc7010047
Cauteruccio FCorradini ETerracina GUrsino DVirgili L(2022)Extraction and analysis of text patterns from NSFW adult content in RedditData & Knowledge Engineering10.1016/j.datak.2022.101979138:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.datak.2022.101979
Rejeb AKeogh JRejeb K(2022)Big data in the food supply chain: a literature reviewJournal of Data, Information and Management10.1007/s42488-021-00064-04:1(33-47)Online publication date: 24-Jan-2022
https://doi.org/10.1007/s42488-021-00064-0
Bonifazi GCecchini SCorradini EGiuliani LUrsino DVirgili L(2022)Extracting time patterns from the lifespans of TikTok challenges to characterize non-dangerous and dangerous onesSocial Network Analysis and Mining10.1007/s13278-022-00893-w12:1Online publication date: 16-Jun-2022
https://doi.org/10.1007/s13278-022-00893-w
Zhu Z(2021)New Media User Behaviour Research Based on Big Data AnalysisJournal of Physics: Conference Series10.1088/1742-6596/1802/4/0420371802:4(042037)Online publication date: 1-Mar-2021
https://doi.org/10.1088/1742-6596/1802/4/042037
Corradini ENocera AUrsino DVirgili L(2021)Investigating the phenomenon of NSFW posts in RedditInformation Sciences: an International Journal10.1016/j.ins.2021.01.062566:C(140-164)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.01.062
Xie JElhoseny MYuan X(2020)Research on Weibo user behavior system for subjective perception and big data mining technologyJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17948438:2(1225-1234)Online publication date: 1-Jan-2020
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