Followee recommendation in Twitter using fuzzy link prediction
In social networking sites, it is useful to receive recommendations about whom to contact or follow. These recommendations not only allow to establish connections with people one might already know in real life, but also with people or users that have similar interests or are potentially interesting...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Blackwell Publishers
2016
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| Subjects: | |
| Online Access: | http://eprints.uanl.mx/27077/1/27077.pdf |
| _version_ | 1824420649751281664 |
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| author | Rodríguez Aldape, Fernando Manuel Torres Treviño, Luis Martín Garza Villarreal, Sara Elena |
| author_facet | Rodríguez Aldape, Fernando Manuel Torres Treviño, Luis Martín Garza Villarreal, Sara Elena |
| author_sort | Rodríguez Aldape, Fernando Manuel |
| collection | Repositorio Institucional |
| description | In social networking sites, it is useful to receive recommendations about whom to contact or follow. These recommendations not only allow to establish connections with people one might already know in real life, but also with people or users that have similar interests or are potentially interesting. We propose an approach that tackles contact (followee) recommendation in Twitter by means of fuzzy logic. This fuzzy approach handles recommendation as a link prediction problem and uses three types of similarity between a pair of users: tweet similarity, followee id similarity, and followee tweet similarity. These similarities are calculated by extracting user profiles. These profiles are, in turn, obtained by considering Twitter as a heterogeneous information network. To test our approach, we crawled a repository of 6,000 users and 2 million tweets, and we measured accuracy by comparing our results with the actual followee lists of the users. These results, which are also compared against the results given by state-of-the-art methods, show a high accuracy. Other advantages of the fuzzy system include a self-explanatory capability and the ability to produce a non-binary friendship value. |
| format | Article |
| id | eprints-27077 |
| institution | UANL |
| language | English |
| publishDate | 2016 |
| publisher | Blackwell Publishers |
| record_format | eprints |
| spelling | eprints-270772024-10-17T19:40:21Z http://eprints.uanl.mx/27077/ Followee recommendation in Twitter using fuzzy link prediction Rodríguez Aldape, Fernando Manuel Torres Treviño, Luis Martín Garza Villarreal, Sara Elena QA Matemáticas, Ciencias computacionales In social networking sites, it is useful to receive recommendations about whom to contact or follow. These recommendations not only allow to establish connections with people one might already know in real life, but also with people or users that have similar interests or are potentially interesting. We propose an approach that tackles contact (followee) recommendation in Twitter by means of fuzzy logic. This fuzzy approach handles recommendation as a link prediction problem and uses three types of similarity between a pair of users: tweet similarity, followee id similarity, and followee tweet similarity. These similarities are calculated by extracting user profiles. These profiles are, in turn, obtained by considering Twitter as a heterogeneous information network. To test our approach, we crawled a repository of 6,000 users and 2 million tweets, and we measured accuracy by comparing our results with the actual followee lists of the users. These results, which are also compared against the results given by state-of-the-art methods, show a high accuracy. Other advantages of the fuzzy system include a self-explanatory capability and the ability to produce a non-binary friendship value. Blackwell Publishers 2016 Article PeerReviewed text en cc_by_nc_nd http://eprints.uanl.mx/27077/1/27077.pdf http://eprints.uanl.mx/27077/1.haspreviewThumbnailVersion/27077.pdf Rodríguez Aldape, Fernando Manuel y Torres Treviño, Luis Martín y Garza Villarreal, Sara Elena (2016) Followee recommendation in Twitter using fuzzy link prediction. Expert systems, 33 (4). pp. 349-361. ISSN 1468-0394 http://doi.org/10.1111/exsy.12153 doi:10.1111/exsy.12153 |
| spellingShingle | QA Matemáticas, Ciencias computacionales Rodríguez Aldape, Fernando Manuel Torres Treviño, Luis Martín Garza Villarreal, Sara Elena Followee recommendation in Twitter using fuzzy link prediction |
| thumbnail | https://rediab.uanl.mx/themes/sandal5/images/online.png |
| title | Followee recommendation in Twitter using fuzzy link prediction |
| title_full | Followee recommendation in Twitter using fuzzy link prediction |
| title_fullStr | Followee recommendation in Twitter using fuzzy link prediction |
| title_full_unstemmed | Followee recommendation in Twitter using fuzzy link prediction |
| title_short | Followee recommendation in Twitter using fuzzy link prediction |
| title_sort | followee recommendation in twitter using fuzzy link prediction |
| topic | QA Matemáticas, Ciencias computacionales |
| url | http://eprints.uanl.mx/27077/1/27077.pdf |
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