Context-Based Collaborative Filtering for Citation Recommendation
Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in v...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2015-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7279056/ |
| _version_ | 1818411821259816960 |
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| author | Haifeng Liu Xiangjie Kong Xiaomei Bai Wei Wang Teshome Megersa Bekele Feng Xia |
| author_facet | Haifeng Liu Xiangjie Kong Xiaomei Bai Wei Wang Teshome Megersa Bekele Feng Xia |
| author_sort | Haifeng Liu |
| collection | DOAJ |
| description | Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list. |
| first_indexed | 2024-12-14T10:37:30Z |
| format | Article |
| id | doaj.art-c04e673fa97444a082e7280af5800db7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:37:30Z |
| publishDate | 2015-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c04e673fa97444a082e7280af5800db72022-12-21T23:05:51ZengIEEEIEEE Access2169-35362015-01-0131695170310.1109/ACCESS.2015.24813207279056Context-Based Collaborative Filtering for Citation RecommendationHaifeng Liu0Xiangjie Kong1https://orcid.org/0000-0003-2592-6830Xiaomei Bai2Wei Wang3Teshome Megersa Bekele4Feng Xia5School of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaCitation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.https://ieeexplore.ieee.org/document/7279056/Citation RecommendationCollaborative FilteringCitation ContextCitation Relation MatrixAssociation Mining |
| spellingShingle | Haifeng Liu Xiangjie Kong Xiaomei Bai Wei Wang Teshome Megersa Bekele Feng Xia Context-Based Collaborative Filtering for Citation Recommendation IEEE Access Citation Recommendation Collaborative Filtering Citation Context Citation Relation Matrix Association Mining |
| title | Context-Based Collaborative Filtering for Citation Recommendation |
| title_full | Context-Based Collaborative Filtering for Citation Recommendation |
| title_fullStr | Context-Based Collaborative Filtering for Citation Recommendation |
| title_full_unstemmed | Context-Based Collaborative Filtering for Citation Recommendation |
| title_short | Context-Based Collaborative Filtering for Citation Recommendation |
| title_sort | context based collaborative filtering for citation recommendation |
| topic | Citation Recommendation Collaborative Filtering Citation Context Citation Relation Matrix Association Mining |
| url | https://ieeexplore.ieee.org/document/7279056/ |
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