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Information 2018, 9(12), 303; https://doi.org/10.3390/info9120303

Evaluating User Behaviour in a Cooperative Environment

1
JAKALA, 20124 Milano, Italy
2
Dimes Department, University of Calabria, 87036 Rende, Italy
3
Subcom, 20124 Milano, Italy
4
Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR-CNR), 87036 Rende, Italy
5
ICT-SUD, 87036 Rende, Italy
6
Alkemy, 20124 Milano, Italy
*
Author to whom correspondence should be addressed.
Received: 15 October 2018 / Revised: 25 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
Full-Text   |   PDF [597 KB, uploaded 30 November 2018]   |  

Abstract

Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new advanced analysis strategies to be devised in order to profitably leverage this information. Moreover, due to the heterogeneous and fast changing nature of these data, we need to leverage new data storage and management tools to effectively store them. In this paper, we analyze the effect of user searches and suggestions and try to understand how much they influence a user’s social environment. This task is crucial to perform efficient identification of the users that are able to spread their influence across the network. Gathering information about user preferences is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestions. We show the application of our approach for a huge research project named D-ALL that stands for Data Alliance. In fact, we tried to assess the reaction of users in a competitive environment when they were invited to judge each other. Our results show that the users tend to conform to each other when no tangible rewards are provided while they try to reduce other users’ ratings when it affects getting a tangible prize. View Full-Text
Keywords: behavioural analysis; big data; Exponential Random Graph Model; clustering; social influence behavioural analysis; big data; Exponential Random Graph Model; clustering; social influence
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Bazzi, E.; Cassavia, N.; Chiggiato, D.; Masciari, E.; Saccà, D.; Spada, A.; Trubitsyna, I. Evaluating User Behaviour in a Cooperative Environment. Information 2018, 9, 303.

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