A Personalized Explainable Learner Implicit Friend Recommendation Method
Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accur...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-01-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41019-023-00204-z |
| _version_ | 1797863953697603584 |
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| author | Chunying Li Bingyang Zhou Weijie Lin Zhikang Tang Yong Tang Yanchun Zhang Jinli Cao |
| author_facet | Chunying Li Bingyang Zhou Weijie Lin Zhikang Tang Yong Tang Yanchun Zhang Jinli Cao |
| author_sort | Chunying Li |
| collection | DOAJ |
| description | Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability. |
| first_indexed | 2024-04-09T22:43:54Z |
| format | Article |
| id | doaj.art-8271b2aec903444eae53546923d49d0b |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-04-09T22:43:54Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-8271b2aec903444eae53546923d49d0b2023-03-22T11:58:37ZengSpringerOpenData Science and Engineering2364-11852364-15412023-01-0181233510.1007/s41019-023-00204-zA Personalized Explainable Learner Implicit Friend Recommendation MethodChunying Li0Bingyang Zhou1Weijie Lin2Zhikang Tang3Yong Tang4Yanchun Zhang5Jinli Cao6School of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer, South China Normal UniversityCyberspace Institute of Advanced Technology, Guangzhou UniversitySchool of Engineering and Mathematical Sciences, LA TROBE UniversityAbstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.https://doi.org/10.1007/s41019-023-00204-zAcademic social networkFriend recommendationPersonalizationExplainable |
| spellingShingle | Chunying Li Bingyang Zhou Weijie Lin Zhikang Tang Yong Tang Yanchun Zhang Jinli Cao A Personalized Explainable Learner Implicit Friend Recommendation Method Data Science and Engineering Academic social network Friend recommendation Personalization Explainable |
| title | A Personalized Explainable Learner Implicit Friend Recommendation Method |
| title_full | A Personalized Explainable Learner Implicit Friend Recommendation Method |
| title_fullStr | A Personalized Explainable Learner Implicit Friend Recommendation Method |
| title_full_unstemmed | A Personalized Explainable Learner Implicit Friend Recommendation Method |
| title_short | A Personalized Explainable Learner Implicit Friend Recommendation Method |
| title_sort | personalized explainable learner implicit friend recommendation method |
| topic | Academic social network Friend recommendation Personalization Explainable |
| url | https://doi.org/10.1007/s41019-023-00204-z |
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