Maximum Recommendation in Geo-social Network for Business
Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2019-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/320433 |
| _version_ | 1797207469067337728 |
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| author | Jing Yu Sanggyun Na Zongmin Cui |
| author_facet | Jing Yu Sanggyun Na Zongmin Cui |
| author_sort | Jing Yu |
| collection | DOAJ |
| description | Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company's recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field. |
| first_indexed | 2024-04-24T09:23:24Z |
| format | Article |
| id | doaj.art-4bc77cdd44f9486395609406a70c8051 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:23:24Z |
| publishDate | 2019-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-4bc77cdd44f9486395609406a70c80512024-04-15T15:30:48ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392019-01-0126243344010.17559/TV-20181105122320Maximum Recommendation in Geo-social Network for BusinessJing Yu0Sanggyun Na1Zongmin Cui2School of Information Science and Technology, Jiujiang University, No. 551, Qianjin East Road, Jiujiang, Jiangxi 332005, China / College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, KoreaCollege of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, KoreaSchool of Information Science and Technology, Jiujiang University, No. 551, Qianjin East Road, Jiujiang, Jiangxi 332005, ChinaMost of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company's recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field.https://hrcak.srce.hr/file/320433business policygeo-social networkmaximum recommendationnegative influence |
| spellingShingle | Jing Yu Sanggyun Na Zongmin Cui Maximum Recommendation in Geo-social Network for Business Tehnički Vjesnik business policy geo-social network maximum recommendation negative influence |
| title | Maximum Recommendation in Geo-social Network for Business |
| title_full | Maximum Recommendation in Geo-social Network for Business |
| title_fullStr | Maximum Recommendation in Geo-social Network for Business |
| title_full_unstemmed | Maximum Recommendation in Geo-social Network for Business |
| title_short | Maximum Recommendation in Geo-social Network for Business |
| title_sort | maximum recommendation in geo social network for business |
| topic | business policy geo-social network maximum recommendation negative influence |
| url | https://hrcak.srce.hr/file/320433 |
| work_keys_str_mv | AT jingyu maximumrecommendationingeosocialnetworkforbusiness AT sanggyunna maximumrecommendationingeosocialnetworkforbusiness AT zongmincui maximumrecommendationingeosocialnetworkforbusiness |