Boosting Memory-Based Collaborative Filtering Using Content-Metadata
Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/4/561 |
| _version_ | 1818038904689786880 |
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| author | Kyung Soo Kim Doo Soo Chang Yong Suk Choi |
| author_facet | Kyung Soo Kim Doo Soo Chang Yong Suk Choi |
| author_sort | Kyung Soo Kim |
| collection | DOAJ |
| description | Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment. |
| first_indexed | 2024-12-10T07:50:09Z |
| format | Article |
| id | doaj.art-5ba4fa3ab40d4ba4857902ab04517edc |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-12-10T07:50:09Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-5ba4fa3ab40d4ba4857902ab04517edc2022-12-22T01:57:03ZengMDPI AGSymmetry2073-89942019-04-0111456110.3390/sym11040561sym11040561Boosting Memory-Based Collaborative Filtering Using Content-MetadataKyung Soo Kim0Doo Soo Chang1Yong Suk Choi2Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Computer Science and Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Computer Science and Engineering, Hanyang University, Seoul 04763, KoreaRecommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.https://www.mdpi.com/2073-8994/11/4/561collaborative filteringcontent-metadatauser-content rating |
| spellingShingle | Kyung Soo Kim Doo Soo Chang Yong Suk Choi Boosting Memory-Based Collaborative Filtering Using Content-Metadata Symmetry collaborative filtering content-metadata user-content rating |
| title | Boosting Memory-Based Collaborative Filtering Using Content-Metadata |
| title_full | Boosting Memory-Based Collaborative Filtering Using Content-Metadata |
| title_fullStr | Boosting Memory-Based Collaborative Filtering Using Content-Metadata |
| title_full_unstemmed | Boosting Memory-Based Collaborative Filtering Using Content-Metadata |
| title_short | Boosting Memory-Based Collaborative Filtering Using Content-Metadata |
| title_sort | boosting memory based collaborative filtering using content metadata |
| topic | collaborative filtering content-metadata user-content rating |
| url | https://www.mdpi.com/2073-8994/11/4/561 |
| work_keys_str_mv | AT kyungsookim boostingmemorybasedcollaborativefilteringusingcontentmetadata AT doosoochang boostingmemorybasedcollaborativefilteringusingcontentmetadata AT yongsukchoi boostingmemorybasedcollaborativefilteringusingcontentmetadata |