Recommendation in location based social networks
Recommendation in Location Based Social Networks (LBSNs) is an emerging research topic. Compared to conventional recommendation systems, there is much more information could be utilized in a LBSN recommendation system. In this report, the author first proposes a new method of utilizing spatial sensi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2013
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| Online Access: | http://hdl.handle.net/10356/52049 |
| _version_ | 1824454055519322112 |
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| author | Chen, Long. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Chen, Long. |
| author_sort | Chen, Long. |
| collection | NTU |
| description | Recommendation in Location Based Social Networks (LBSNs) is an emerging research topic. Compared to conventional recommendation systems, there is much more information could be utilized in a LBSN recommendation system. In this report, the author first proposes a new method of utilizing spatial sensitivity to generate dynamic weightage average score for combining the User Collaborative Filtering and the Geographical Influence in a personalized fashion for each single user. Temporal information in check-ins which interconnects users and point-of-interests have significant value in LBSNs. In this report, the author also discusses how to leverage temporal information in the User Collaborative Filtering and the Geographical Influence. Finally, a unified framework for all proposed methods is defined in this report. A detailed empirical experiment is included in this report which provides a comprehensive comparison of the performance of proposed methods against baseline methods. |
| first_indexed | 2025-02-19T03:16:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/52049 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:16:14Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/520492023-03-03T20:54:55Z Recommendation in location based social networks Chen, Long. School of Computer Engineering Centre for Advanced Information Systems Cong Gao DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Recommendation in Location Based Social Networks (LBSNs) is an emerging research topic. Compared to conventional recommendation systems, there is much more information could be utilized in a LBSN recommendation system. In this report, the author first proposes a new method of utilizing spatial sensitivity to generate dynamic weightage average score for combining the User Collaborative Filtering and the Geographical Influence in a personalized fashion for each single user. Temporal information in check-ins which interconnects users and point-of-interests have significant value in LBSNs. In this report, the author also discusses how to leverage temporal information in the User Collaborative Filtering and the Geographical Influence. Finally, a unified framework for all proposed methods is defined in this report. A detailed empirical experiment is included in this report which provides a comprehensive comparison of the performance of proposed methods against baseline methods. Bachelor of Engineering (Computer Engineering) 2013-04-22T02:46:27Z 2013-04-22T02:46:27Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/52049 en Nanyang Technological University 45 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Chen, Long. Recommendation in location based social networks |
| title | Recommendation in location based social networks |
| title_full | Recommendation in location based social networks |
| title_fullStr | Recommendation in location based social networks |
| title_full_unstemmed | Recommendation in location based social networks |
| title_short | Recommendation in location based social networks |
| title_sort | recommendation in location based social networks |
| topic | DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications |
| url | http://hdl.handle.net/10356/52049 |
| work_keys_str_mv | AT chenlong recommendationinlocationbasedsocialnetworks |