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General Smart City Experts’ Perceptions of Citizen Participation: A Questionnaire Survey Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Improving Recommendations Using Traceability and Machine Learning

Authors : Kamal Souali, Othmane Rahmaoui, Mohammed Ouzzif

Published in: Innovations in Smart Cities Applications Edition 3

Publisher: Springer International Publishing

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Abstract

The intelligent filtering systems known as Recommender systems, are important tools meant to assist users in their choices and solve the issue of information overload, as they can predict and provide suggestions, intended to meet users’ interests and expectations. For this purpose, the recommendation process uses different data which is extracted from the users’ preferences and interests (User Profile) and the items’ characteristics. These systems employ the same techniques but different approaches that exploit textual and history data from users’ activities, taken either from their profiles or from other valuable resources and are often combined to provide the most suitable results. However, due to the increasing volume of data, the recommendation becomes more redundant and the same elements get proposed repeatedly. In this paper, we intend to discuss the benefits of using traceability and machine learning and how they can improve the recommendation and make it more efficient and reliable.

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Metadata
Title
Improving Recommendations Using Traceability and Machine Learning
Authors
Kamal Souali
Othmane Rahmaoui
Mohammed Ouzzif
Copyright Year
2020
Book
Innovations in Smart Cities Applications Edition 3

Print ISBN: 978-3-030-37628-4

Electronic ISBN: 978-3-030-37629-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37629-1_76

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