research-article

The Positive Role of Negative Ratings for Recommender

Authors:

Peng ZhangAuthors Info & Claims

ICCAE '17: Proceedings of the 9th International Conference on Computer and Automation Engineering

Pages 89 - 93

https://doi.org/10.1145/3057039.3057047

Published: 18 February 2017 Publication History

Abstract

In this paper, inspired by the network-based inference, a new weighted approach is presented to experimentally assess the role of negative data. This weighted approach is conductive to distinguish the contributions from positive and negative ratings. By conducting the positive and negative data with twofold weights, the method relative to NBI and NBIS can obtain a bigger precision and a smaller ranking score, leading to a better recommendation quality. Via the further numerical tests on three benchmark datasets, the results show that the presented approach can better reveal the positive role of negative ratings for improving the recommendation quality. Moreover, by using some appropriate tools, the positive recommendation role of negative data will strengthen, and thoughtlessly removing negative data not only miss some valuable information, but also can weaken the quality of recommendation system.

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Cited By

Albatayneh NGhauth KChua F(2022)Discriminate2Rec: Negation-based dynamic discriminative interest-based preference learning for semantics-aware content-based recommendationExpert Systems with Applications10.1016/j.eswa.2022.116988199(116988)Online publication date: Aug-2022
https://doi.org/10.1016/j.eswa.2022.116988

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ICCAE '17: Proceedings of the 9th International Conference on Computer and Automation Engineering

February 2017

365 pages

ISBN:9781450348096

DOI:10.1145/3057039

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 ACM 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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Macquarie U., Austarlia

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Published: 18 February 2017

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ICCAE '17

ICCAE '17: 9th International Conference on Computer and Automation Engineering

February 18 - 21, 2017

Sydney, Australia

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Cited By

Albatayneh NGhauth KChua F(2022)Discriminate2Rec: Negation-based dynamic discriminative interest-based preference learning for semantics-aware content-based recommendationExpert Systems with Applications10.1016/j.eswa.2022.116988199(116988)Online publication date: Aug-2022
https://doi.org/10.1016/j.eswa.2022.116988

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