A Social TopN Recommendation Scheme Based on Grey Forecast Model.
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| Title: | A Social TopN Recommendation Scheme Based on Grey Forecast Model. |
|---|---|
| Authors: | Yunpeng Xiao1 xiaoyp@cqupt.edu.cn, Keyi Zhang1, Ming Xu2, Yanbing Liu1 |
| Source: | Journal of Grey System. 2018, Vol. 30 Issue 4, p78-96. 19p. |
| Subjects: | Gray forecasting model, Social network theory, Discretization methods, Algorithms, Standard deviations |
| Abstract: | In view of data nonuniformity and sparseness existing in recommendation schemes, this study introduces and optimizes grey system theory model in application scenarios of social network. Furthermore, a new topN recommendation scheme is proposed. Firstly, by analyzing the rating behavior of users, the factors that affect rating are discovered. To quantify the factors, time discretization method is leveraged as well as three aspects in recommendation research: user, item and context. Secondly, in regard to the problem of time nonuniformity in observed sequences, this paper changes the non-uniform sequences into equal interval, which optimizes the GM(1,N) model of grey system and expands its application scope. Finally, considering the timeliness of user preference, a time decay function is introduced for the equal interval sequences to optimize the grey forecast model and reduce its error rate in prediction. Besides, the improved grey forecast model is applied to mine the explicit relationship between user rating and relatedfactors, and construct rating prediction algorithm to improve the recommendation accuracy on the interest list of target users. The experimental results reveal the efficiency of our algorithm both in mean absolute error (MAE) and root mean square error (RMSE). Moreover, the proposed recommendation scheme has favorable precision, recall and F-measure. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Grey System is the property of Research Information Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 134557971 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Social TopN Recommendation Scheme Based on Grey Forecast Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yunpeng+Xiao%22">Yunpeng Xiao</searchLink><relatesTo>1</relatesTo><i> xiaoyp@cqupt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Keyi+Zhang%22">Keyi Zhang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ming+Xu%22">Ming Xu</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yanbing+Liu%22">Yanbing Liu</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Grey+System%22">Journal of Grey System</searchLink>. 2018, Vol. 30 Issue 4, p78-96. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gray+forecasting+model%22">Gray forecasting model</searchLink><br /><searchLink fieldCode="DE" term="%22Social+network+theory%22">Social network theory</searchLink><br /><searchLink fieldCode="DE" term="%22Discretization+methods%22">Discretization methods</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In view of data nonuniformity and sparseness existing in recommendation schemes, this study introduces and optimizes grey system theory model in application scenarios of social network. Furthermore, a new topN recommendation scheme is proposed. Firstly, by analyzing the rating behavior of users, the factors that affect rating are discovered. To quantify the factors, time discretization method is leveraged as well as three aspects in recommendation research: user, item and context. Secondly, in regard to the problem of time nonuniformity in observed sequences, this paper changes the non-uniform sequences into equal interval, which optimizes the GM(1,N) model of grey system and expands its application scope. Finally, considering the timeliness of user preference, a time decay function is introduced for the equal interval sequences to optimize the grey forecast model and reduce its error rate in prediction. Besides, the improved grey forecast model is applied to mine the explicit relationship between user rating and relatedfactors, and construct rating prediction algorithm to improve the recommendation accuracy on the interest list of target users. The experimental results reveal the efficiency of our algorithm both in mean absolute error (MAE) and root mean square error (RMSE). Moreover, the proposed recommendation scheme has favorable precision, recall and F-measure. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Grey System is the property of Research Information Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 78 Subjects: – SubjectFull: Gray forecasting model Type: general – SubjectFull: Social network theory Type: general – SubjectFull: Discretization methods Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Standard deviations Type: general Titles: – TitleFull: A Social TopN Recommendation Scheme Based on Grey Forecast Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yunpeng Xiao – PersonEntity: Name: NameFull: Keyi Zhang – PersonEntity: Name: NameFull: Ming Xu – PersonEntity: Name: NameFull: Yanbing Liu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 09573720 Numbering: – Type: volume Value: 30 – Type: issue Value: 4 Titles: – TitleFull: Journal of Grey System Type: main |
| ResultId | 1 |
