Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison
Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/1/42 |
| _version_ | 1827664957796253696 |
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| author | Yong Zheng |
| author_facet | Yong Zheng |
| author_sort | Yong Zheng |
| collection | DOAJ |
| description | Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets. |
| first_indexed | 2024-03-10T01:15:52Z |
| format | Article |
| id | doaj.art-1fb452626dfd432abd3604dcafff98e2 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T01:15:52Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-1fb452626dfd432abd3604dcafff98e22023-11-23T14:08:51ZengMDPI AGInformation2078-24892022-01-011314210.3390/info13010042Context-Aware Collaborative Filtering Using Context Similarity: An Empirical ComparisonYong Zheng0Department of Information Technology and Management, College of Computing, Illinois Institute of Technology, Chicago, IL 60616, USARecommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets.https://www.mdpi.com/2078-2489/13/1/42recommender systemscontext-awarecontext similaritycollaborative filtering |
| spellingShingle | Yong Zheng Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison Information recommender systems context-aware context similarity collaborative filtering |
| title | Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison |
| title_full | Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison |
| title_fullStr | Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison |
| title_full_unstemmed | Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison |
| title_short | Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison |
| title_sort | context aware collaborative filtering using context similarity an empirical comparison |
| topic | recommender systems context-aware context similarity collaborative filtering |
| url | https://www.mdpi.com/2078-2489/13/1/42 |
| work_keys_str_mv | AT yongzheng contextawarecollaborativefilteringusingcontextsimilarityanempiricalcomparison |