Communication-Based Book Recommendation in Computational Social Systems
This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6651493 |
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| _version_ | 1832568483948265472 |
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| author | Long Zuo Shuo Xiong Xin Qi Zheng Wen Yiwen Tang |
| author_facet | Long Zuo Shuo Xiong Xin Qi Zheng Wen Yiwen Tang |
| author_sort | Long Zuo |
| collection | DOAJ |
| description | This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm. |
| format | Article |
| id | doaj-art-2e1135fb306e4d1c9548f40d2376a59b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2e1135fb306e4d1c9548f40d2376a59b2025-02-03T00:58:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66514936651493Communication-Based Book Recommendation in Computational Social SystemsLong Zuo0Shuo Xiong1Xin Qi2Zheng Wen3Yiwen Tang4Chang’an University, Xi’an, ChinaHuazhong University of Science and Technology, Wuhan, ChinaWaseda Univeristy, Tokyo, JapanWaseda Univeristy, Tokyo, JapanHuazhong University of Science and Technology, Wuhan, ChinaThis paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.http://dx.doi.org/10.1155/2021/6651493 |
| spellingShingle | Long Zuo Shuo Xiong Xin Qi Zheng Wen Yiwen Tang Communication-Based Book Recommendation in Computational Social Systems Complexity |
| title | Communication-Based Book Recommendation in Computational Social Systems |
| title_full | Communication-Based Book Recommendation in Computational Social Systems |
| title_fullStr | Communication-Based Book Recommendation in Computational Social Systems |
| title_full_unstemmed | Communication-Based Book Recommendation in Computational Social Systems |
| title_short | Communication-Based Book Recommendation in Computational Social Systems |
| title_sort | communication based book recommendation in computational social systems |
| url | http://dx.doi.org/10.1155/2021/6651493 |
| work_keys_str_mv | AT longzuo communicationbasedbookrecommendationincomputationalsocialsystems AT shuoxiong communicationbasedbookrecommendationincomputationalsocialsystems AT xinqi communicationbasedbookrecommendationincomputationalsocialsystems AT zhengwen communicationbasedbookrecommendationincomputationalsocialsystems AT yiwentang communicationbasedbookrecommendationincomputationalsocialsystems |