Publication: BRScS: a hybrid recommendation model fusing multi-source heterogeneous data
| dc.contributor.author | Ji, Zhenyan | |
| dc.contributor.author | Yang, Chun | |
| dc.contributor.author | Wang, Huihui | |
| dc.contributor.author | Armendáriz Íñigo, José Enrique | |
| dc.contributor.author | Arce Urriza, Marta | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Gestión de Empresas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Enpresen Kudeaketa | eu |
| dc.date.accessioned | 2021-02-02T09:41:09Z | |
| dc.date.available | 2021-02-02T09:41:09Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually affected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, and HRSc. The BRScS model is also scalable and can fuse new types of data easily. | en |
| dc.description.sponsorship | This work was supported by the Research Center of Software Engineering in the School of Software Engineering, Beijing Jiaotong University. This work is supported by the National Key Research and Development Program of China under grant no. 2018YFC0831903 and Major Project of National Natural Science Foundation of China under grant no. 51935002. | en |
| dc.format.extent | 17 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1186/s13638-020-01716-2 | |
| dc.identifier.issn | 1687-1499 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/39126 | |
| dc.language.iso | eng | en |
| dc.publisher | SpringerOpen | en |
| dc.relation.ispartof | EURASIP Journal on Wireless Communications and Networking, 2020, 124 | en |
| dc.relation.publisherversion | https://doi.org/10.1186/s13638-020-01716-2 | |
| dc.rights | © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Multi-source heterogeneous data | en |
| dc.subject | Recommendation model | en |
| dc.subject | Social network | en |
| dc.title | BRScS: a hybrid recommendation model fusing multi-source heterogeneous data | en |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type | Artículo / Artikulua | es |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dspace.entity.type | Publication | |
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