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2019 | OriginalPaper | Buchkapitel

Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems

verfasst von : Georges Chaaya, Jacques Bou Abdo, Elisabeth Métais, Raja Chiky, Jacques Demerjian, Kablan Barbar

Erschienen in: Information Systems

Verlag: Springer International Publishing

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Abstract

The study of recommender systems is essential nowadays due to its great effect on businesses and customer satisfaction. Different active learning strategies were previously developed to gain ratings from the users on specific items, and this enables the system to have more information and consequently make more accurate recommendations. In previous studies, these strategies were evaluated using a different selection of metrics in each work, and the experimentations were done on different datasets. In this paper, we solve these weaknesses by comparing the main ten non-personalized strategies on a fair ground, by simulating them against two datasets and using seven of the mostly agreed upon metrics. This gives more trust and less biased results when comparing their performances. Also, the analysis of the computation time and the elicitation efficiency is added.

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Vorheriges Kapitel Limiting the Impact of Statistics as a Proverbial Source of Falsehood

Nächstes Kapitel Board Interlocking and IT Governance: Proposed Conceptual Model

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Titel: Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems
verfasst von: Georges Chaaya
Jacques Bou Abdo
Elisabeth Métais
Raja Chiky
Jacques Demerjian
Kablan Barbar
Verlag: Springer International Publishing
Buch: Information Systems
Print ISBN: 978-3-030-11394-0

Electronic ISBN: 978-3-030-11395-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-11395-7_34

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