SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework
<p dir="ltr">Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users’ needs and preferences can be challenging. However, mobile app rec...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513527470882816 |
|---|---|
| author | Daksh Dave (17949239) |
| author2 | Aditya Sharma (368820) Shafi’i Muhammad Abdulhamid (3158544) Adeel Ahmed (11823440) Adnan Akhunzada (3134064) Rashid Amin (1389156) |
| author2_role | author author author author author |
| author_facet | Daksh Dave (17949239) Aditya Sharma (368820) Shafi’i Muhammad Abdulhamid (3158544) Adeel Ahmed (11823440) Adnan Akhunzada (3134064) Rashid Amin (1389156) |
| author_role | author |
| dc.creator.none.fl_str_mv | Daksh Dave (17949239) Aditya Sharma (368820) Shafi’i Muhammad Abdulhamid (3158544) Adeel Ahmed (11823440) Adnan Akhunzada (3134064) Rashid Amin (1389156) |
| dc.date.none.fl_str_mv | 2023-07-18T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3296466 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/SAppKG_Mobile_App_Recommendation_Using_Knowledge_Graph_and_Side_Information-A_Secure_Framework/25205246 |
| 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 Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Mobile applications Recommender systems Privacy Knowledge graphs Data models Internet Data privacy Smart phones Security management Information security link prediction semantic information |
| dc.title.none.fl_str_mv | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users’ needs and preferences can be challenging. However, mobile app recommender systems have emerged as a helpful tool in simplifying this process. But there is a drawback to employing app recommender systems. These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. We address this issue by developing SAppKG, an end-to- end user privacy-preserving knowledge graph architecture for mobile app recommendation based on knowledge graph models such as SAppKG-S and SAppKG-D, that utilized the interaction data and side information of app attributes. We tested the proposed model on real-world data from the Google Play app store, using precision, recall, mean absolute precision, and mean reciprocal rank. We found that the proposed model improved results on all four metrics. We also compared the proposed model to baseline models and found that it outperformed them on all four metrics.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/10.1109/access.2023.3296466" target="_blank">https://dx.doi.org/10.1109/access.2023.3296466</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_719d8745688e355901c7488ffa560e32 |
| identifier_str_mv | 10.1109/access.2023.3296466 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25205246 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure FrameworkDaksh Dave (17949239)Aditya Sharma (368820)Shafi’i Muhammad Abdulhamid (3158544)Adeel Ahmed (11823440)Adnan Akhunzada (3134064)Rashid Amin (1389156)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningMobile applicationsRecommender systemsPrivacyKnowledge graphsData modelsInternetData privacySmart phonesSecurity managementInformation securitylink predictionsemantic information<p dir="ltr">Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users’ needs and preferences can be challenging. However, mobile app recommender systems have emerged as a helpful tool in simplifying this process. But there is a drawback to employing app recommender systems. These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. We address this issue by developing SAppKG, an end-to- end user privacy-preserving knowledge graph architecture for mobile app recommendation based on knowledge graph models such as SAppKG-S and SAppKG-D, that utilized the interaction data and side information of app attributes. We tested the proposed model on real-world data from the Google Play app store, using precision, recall, mean absolute precision, and mean reciprocal rank. We found that the proposed model improved results on all four metrics. We also compared the proposed model to baseline models and found that it outperformed them on all four metrics.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/10.1109/access.2023.3296466" target="_blank">https://dx.doi.org/10.1109/access.2023.3296466</a></p>2023-07-18T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3296466https://figshare.com/articles/journal_contribution/SAppKG_Mobile_App_Recommendation_Using_Knowledge_Graph_and_Side_Information-A_Secure_Framework/25205246CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252052462023-07-18T06:00:00Z |
| spellingShingle | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework Daksh Dave (17949239) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Mobile applications Recommender systems Privacy Knowledge graphs Data models Internet Data privacy Smart phones Security management Information security link prediction semantic information |
| status_str | publishedVersion |
| title | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| title_full | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| title_fullStr | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| title_full_unstemmed | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| title_short | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| title_sort | SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Mobile applications Recommender systems Privacy Knowledge graphs Data models Internet Data privacy Smart phones Security management Information security link prediction semantic information |