Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance
Research conducted on social media is currently increasing. Information obtained by users of social media has resulted in the development of many recommendation systems that analyze user preferences in an attempt to locate the most suitable products for recommendation to users. A great number of peo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8410908/ |
| _version_ | 1818853906640273408 |
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| author | Jui-Hung Chang Chen-En Tsai Jung-Hsien Chiang |
| author_facet | Jui-Hung Chang Chen-En Tsai Jung-Hsien Chiang |
| author_sort | Jui-Hung Chang |
| collection | DOAJ |
| description | Research conducted on social media is currently increasing. Information obtained by users of social media has resulted in the development of many recommendation systems that analyze user preferences in an attempt to locate the most suitable products for recommendation to users. A great number of people have used social media platforms, such as Twitter, to develop a hotel recommendation system. Here, we propose a Twitter-based recommendation system via the aid of heterogeneous social media. First, a model is designed to predict user preferences by improving matrix factorization based on user preferences and users' personal data, where basic information about hotels collected from Yelp is used as auxiliary information. On the other hand, an analytical user posting behavior algorithm is created for establishing users' posting behavior vectors based on earlier posts in Twitter and Yelp. This results of the experiments show that the proposed method can improve accuracy by 30% in terms of RECALL compared with the Twitter-based recommendation system without the use of heterogeneous social media. Furthermore, it can improve the accuracy of the mean reciprocal rank by 80% and can increase precision by as much as 100%. |
| first_indexed | 2024-12-19T07:44:16Z |
| format | Article |
| id | doaj.art-ee7398fcdd274bbdac79dcfa17d3da75 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:44:16Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ee7398fcdd274bbdac79dcfa17d3da752022-12-21T20:30:23ZengIEEEIEEE Access2169-35362018-01-016426474266010.1109/ACCESS.2018.28556908410908Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation PerformanceJui-Hung Chang0https://orcid.org/0000-0002-3735-8853Chen-En Tsai1Jung-Hsien Chiang2Department of Computer Science and Information Engineering, Computer and Network Center, National Cheng Kung University, Tainan, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan, TaiwanResearch conducted on social media is currently increasing. Information obtained by users of social media has resulted in the development of many recommendation systems that analyze user preferences in an attempt to locate the most suitable products for recommendation to users. A great number of people have used social media platforms, such as Twitter, to develop a hotel recommendation system. Here, we propose a Twitter-based recommendation system via the aid of heterogeneous social media. First, a model is designed to predict user preferences by improving matrix factorization based on user preferences and users' personal data, where basic information about hotels collected from Yelp is used as auxiliary information. On the other hand, an analytical user posting behavior algorithm is created for establishing users' posting behavior vectors based on earlier posts in Twitter and Yelp. This results of the experiments show that the proposed method can improve accuracy by 30% in terms of RECALL compared with the Twitter-based recommendation system without the use of heterogeneous social media. Furthermore, it can improve the accuracy of the mean reciprocal rank by 80% and can increase precision by as much as 100%.https://ieeexplore.ieee.org/document/8410908/Analytical user posting behavior algorithmhotel recommendation systemmean reciprocal rank |
| spellingShingle | Jui-Hung Chang Chen-En Tsai Jung-Hsien Chiang Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance IEEE Access Analytical user posting behavior algorithm hotel recommendation system mean reciprocal rank |
| title | Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance |
| title_full | Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance |
| title_fullStr | Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance |
| title_full_unstemmed | Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance |
| title_short | Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance |
| title_sort | using heterogeneous social media as auxiliary information to improve hotel recommendation performance |
| topic | Analytical user posting behavior algorithm hotel recommendation system mean reciprocal rank |
| url | https://ieeexplore.ieee.org/document/8410908/ |
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