A novel interest drift sensitivity academic paper recommender based on implicit feedback
Academic recommendation systems have been rapidly developed in recent years, helping researchers to find favorite paper. However, traditional methods applied to paper recommendation face more challenges. First, users can only read a small number of papers, resulting in a very sparse user-paper matri...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001014 |
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| _version_ | 1846122195118456832 |
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| author | Weiming Huang Baisong Liu Zhaoliang Wang |
| author_facet | Weiming Huang Baisong Liu Zhaoliang Wang |
| author_sort | Weiming Huang |
| collection | DOAJ |
| description | Academic recommendation systems have been rapidly developed in recent years, helping researchers to find favorite paper. However, traditional methods applied to paper recommendation face more challenges. First, users can only read a small number of papers, resulting in a very sparse user-paper matrix, but the method based on random sampling of negative samples is ineffective due to the uncertainty of negative samples. And users’ academic interests shift frequently, so the approach that ignores temporal information is not applicable. To overcome the above challenges, this paper proposes an implicit feedback-based interest drift-aware academic paper recommendation algorithm. The algorithm explicitly integrates the user’s interest drift into the model through regularization. The algorithm alleviates sparsity by introducing contextual information through a multiplicative law and significantly reduces the computational complexity by using a caching approach. Experimental results on two real paper recommendation datasets show that the proposed method outperforms current methods in terms of recommendation accuracy and computational efficiency. |
| format | Article |
| id | doaj-art-9ee69e7ddd6c4e7eac91651d609b7a9b |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-9ee69e7ddd6c4e7eac91651d609b7a9b2024-12-15T06:14:45ZengElsevierEgyptian Informatics Journal1110-86652024-12-0128100538A novel interest drift sensitivity academic paper recommender based on implicit feedbackWeiming Huang0Baisong Liu1Zhaoliang Wang2Ningbo University, Fenghua Street, Ningbo, 315211, Zhejiang Province, China; Inner Mongolia Metal Material Research Institute, Qingshan Street, Baotou, 014000, Inner Mongolia Province, ChinaNingbo University, Fenghua Street, Ningbo, 315211, Zhejiang Province, China; Corresponding author.Ningbo University, Fenghua Street, Ningbo, 315211, Zhejiang Province, ChinaAcademic recommendation systems have been rapidly developed in recent years, helping researchers to find favorite paper. However, traditional methods applied to paper recommendation face more challenges. First, users can only read a small number of papers, resulting in a very sparse user-paper matrix, but the method based on random sampling of negative samples is ineffective due to the uncertainty of negative samples. And users’ academic interests shift frequently, so the approach that ignores temporal information is not applicable. To overcome the above challenges, this paper proposes an implicit feedback-based interest drift-aware academic paper recommendation algorithm. The algorithm explicitly integrates the user’s interest drift into the model through regularization. The algorithm alleviates sparsity by introducing contextual information through a multiplicative law and significantly reduces the computational complexity by using a caching approach. Experimental results on two real paper recommendation datasets show that the proposed method outperforms current methods in terms of recommendation accuracy and computational efficiency.http://www.sciencedirect.com/science/article/pii/S1110866524001014Citation networkImplicit feedbackTopic modelInterest driftComputational complexity |
| spellingShingle | Weiming Huang Baisong Liu Zhaoliang Wang A novel interest drift sensitivity academic paper recommender based on implicit feedback Egyptian Informatics Journal Citation network Implicit feedback Topic model Interest drift Computational complexity |
| title | A novel interest drift sensitivity academic paper recommender based on implicit feedback |
| title_full | A novel interest drift sensitivity academic paper recommender based on implicit feedback |
| title_fullStr | A novel interest drift sensitivity academic paper recommender based on implicit feedback |
| title_full_unstemmed | A novel interest drift sensitivity academic paper recommender based on implicit feedback |
| title_short | A novel interest drift sensitivity academic paper recommender based on implicit feedback |
| title_sort | novel interest drift sensitivity academic paper recommender based on implicit feedback |
| topic | Citation network Implicit feedback Topic model Interest drift Computational complexity |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001014 |
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