A collaborative filtering recommendation framework utilizing social networks
<p dir="ltr">Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recomm...
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2023
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| _version_ | 1864513560026021888 |
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| author | Aamir Fareed (17019087) |
| author2 | Saima Hassan (14918003) Samir Brahim Belhaouari (9427347) Zahid Halim (17019090) |
| author2_role | author author author |
| author_facet | Aamir Fareed (17019087) Saima Hassan (14918003) Samir Brahim Belhaouari (9427347) Zahid Halim (17019090) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aamir Fareed (17019087) Saima Hassan (14918003) Samir Brahim Belhaouari (9427347) Zahid Halim (17019090) |
| dc.date.none.fl_str_mv | 2023-12-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.mlwa.2023.100495 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_collaborative_filtering_recommendation_framework_utilizing_social_networks/24174213 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Machine learning Recommendation systems Collaborative filtering Social networks Data sparsity |
| dc.title.none.fl_str_mv | A collaborative filtering recommendation framework utilizing social networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent recommendations. The framework is based on a modified version of the user-based collaborative filtering algorithm, which computes user similarity based on their ratings and social connections. The similarity measure is determined by a weighted combination of these two factors, with the weights learned through an optimization process. The framework is evaluated using a dataset of movie ratings and social connections between users. The findings reveal that the proposed approach outperforms traditional collaborative filtering methods regarding recommendation accuracy and relevance. Moreover, the framework can offer more diverse recommendations compared to traditional methods. In summary, the proposed framework integrates social networks to enhance the accuracy and relevance of collaborative filtering recommendations. The approach has various applications, including e-commerce, music, and movie recommendation, and can potentially address the issues of cold-start and sparsity in collaborative filtering.</p><h2>Other Information</h2><p dir="ltr">Published in: Machine Learning with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.mlwa.2023.100495" target="_blank">https://dx.doi.org/10.1016/j.mlwa.2023.100495</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0d96459de73f9ba4d710272a06165525 |
| identifier_str_mv | 10.1016/j.mlwa.2023.100495 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24174213 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A collaborative filtering recommendation framework utilizing social networksAamir Fareed (17019087)Saima Hassan (14918003)Samir Brahim Belhaouari (9427347)Zahid Halim (17019090)Information and computing sciencesData management and data scienceMachine learningRecommendation systemsCollaborative filteringSocial networksData sparsity<p dir="ltr">Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent recommendations. The framework is based on a modified version of the user-based collaborative filtering algorithm, which computes user similarity based on their ratings and social connections. The similarity measure is determined by a weighted combination of these two factors, with the weights learned through an optimization process. The framework is evaluated using a dataset of movie ratings and social connections between users. The findings reveal that the proposed approach outperforms traditional collaborative filtering methods regarding recommendation accuracy and relevance. Moreover, the framework can offer more diverse recommendations compared to traditional methods. In summary, the proposed framework integrates social networks to enhance the accuracy and relevance of collaborative filtering recommendations. The approach has various applications, including e-commerce, music, and movie recommendation, and can potentially address the issues of cold-start and sparsity in collaborative filtering.</p><h2>Other Information</h2><p dir="ltr">Published in: Machine Learning with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.mlwa.2023.100495" target="_blank">https://dx.doi.org/10.1016/j.mlwa.2023.100495</a></p>2023-12-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.mlwa.2023.100495https://figshare.com/articles/journal_contribution/A_collaborative_filtering_recommendation_framework_utilizing_social_networks/24174213CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/241742132023-12-15T00:00:00Z |
| spellingShingle | A collaborative filtering recommendation framework utilizing social networks Aamir Fareed (17019087) Information and computing sciences Data management and data science Machine learning Recommendation systems Collaborative filtering Social networks Data sparsity |
| status_str | publishedVersion |
| title | A collaborative filtering recommendation framework utilizing social networks |
| title_full | A collaborative filtering recommendation framework utilizing social networks |
| title_fullStr | A collaborative filtering recommendation framework utilizing social networks |
| title_full_unstemmed | A collaborative filtering recommendation framework utilizing social networks |
| title_short | A collaborative filtering recommendation framework utilizing social networks |
| title_sort | A collaborative filtering recommendation framework utilizing social networks |
| topic | Information and computing sciences Data management and data science Machine learning Recommendation systems Collaborative filtering Social networks Data sparsity |