Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering
Memory-based collaborative filtering selects the top- k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/847965 |
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| _version_ | 1849695827409240064 |
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| author | Hyeong-Joon Kwon Kwang Seok Hong |
| author_facet | Hyeong-Joon Kwon Kwang Seok Hong |
| author_sort | Hyeong-Joon Kwon |
| collection | DOAJ |
| description | Memory-based collaborative filtering selects the top- k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them. The proposed method minimizes the similarity evaluation errors with the existing neighbor selection method by considering the number of common items between two objects. The method is effective for the practical application of collaborative filtering. For validation, we analyze and compare experimental results between an existing method and the proposed method. We were able to confirm that the proposed method can improve the prediction accuracy of memory-based collaborative filtering by neighbor selection that prioritizes the number of common items. |
| format | Article |
| id | doaj-art-97ea9920b26f4447bb5e68f8ff80da2f |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-97ea9920b26f4447bb5e68f8ff80da2f2025-08-20T03:19:38ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-08-01910.1155/2013/847965Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative FilteringHyeong-Joon KwonKwang Seok HongMemory-based collaborative filtering selects the top- k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them. The proposed method minimizes the similarity evaluation errors with the existing neighbor selection method by considering the number of common items between two objects. The method is effective for the practical application of collaborative filtering. For validation, we analyze and compare experimental results between an existing method and the proposed method. We were able to confirm that the proposed method can improve the prediction accuracy of memory-based collaborative filtering by neighbor selection that prioritizes the number of common items.https://doi.org/10.1155/2013/847965 |
| spellingShingle | Hyeong-Joon Kwon Kwang Seok Hong Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering International Journal of Distributed Sensor Networks |
| title | Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering |
| title_full | Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering |
| title_fullStr | Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering |
| title_full_unstemmed | Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering |
| title_short | Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering |
| title_sort | novel neighbor selection method to improve data sparsity problem in collaborative filtering |
| url | https://doi.org/10.1155/2013/847965 |
| work_keys_str_mv | AT hyeongjoonkwon novelneighborselectionmethodtoimprovedatasparsityproblemincollaborativefiltering AT kwangseokhong novelneighborselectionmethodtoimprovedatasparsityproblemincollaborativefiltering |