Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity
Context-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users’ specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-08-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1775011 |
| _version_ | 1797684908317999104 |
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| author | Sonal Linda Sonajharia Minz K.K. Bharadwaj |
| author_facet | Sonal Linda Sonajharia Minz K.K. Bharadwaj |
| author_sort | Sonal Linda |
| collection | DOAJ |
| description | Context-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users’ specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equal importance to all contextual dimensions is not appropriate for seeking an intelligent and useful recommendation. In this paper, we propose a Real-coded Genetic Algorithm (RCGA) based CARS framework that exploits contextual pre-filtering and contextual modeling paradigms into CACF with appropriate context feature weights for enhancing accuracy as well as the diversity of the recommendation list. Further to alleviate the data sparsity, an effective missing value prediction (EMVP) algorithm is applied into proposed framework. The accuracy based on RCGA is compared with other two schemes: Support Vector Machine (SVM) and Particle Swarm Optimization (PSO), and RCGA has shown better results. Experimental results based on real-world datasets have clearly established the effectiveness of our proposed CARS schemes. |
| first_indexed | 2024-03-12T00:36:33Z |
| format | Article |
| id | doaj.art-cd48aa1044b54cb895bb8972d02468dd |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:33Z |
| publishDate | 2020-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-cd48aa1044b54cb895bb8972d02468dd2023-09-15T09:33:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452020-08-01341073075310.1080/08839514.2020.17750111775011Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data SparsitySonal Linda0Sonajharia Minz1K.K. Bharadwaj2Jawaharlal Nehru UniversityJawaharlal Nehru UniversityJawaharlal Nehru UniversityContext-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users’ specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equal importance to all contextual dimensions is not appropriate for seeking an intelligent and useful recommendation. In this paper, we propose a Real-coded Genetic Algorithm (RCGA) based CARS framework that exploits contextual pre-filtering and contextual modeling paradigms into CACF with appropriate context feature weights for enhancing accuracy as well as the diversity of the recommendation list. Further to alleviate the data sparsity, an effective missing value prediction (EMVP) algorithm is applied into proposed framework. The accuracy based on RCGA is compared with other two schemes: Support Vector Machine (SVM) and Particle Swarm Optimization (PSO), and RCGA has shown better results. Experimental results based on real-world datasets have clearly established the effectiveness of our proposed CARS schemes.http://dx.doi.org/10.1080/08839514.2020.1775011 |
| spellingShingle | Sonal Linda Sonajharia Minz K.K. Bharadwaj Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity Applied Artificial Intelligence |
| title | Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity |
| title_full | Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity |
| title_fullStr | Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity |
| title_full_unstemmed | Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity |
| title_short | Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity |
| title_sort | effective context aware recommendations based on context weighting using genetic algorithm and alleviating data sparsity |
| url | http://dx.doi.org/10.1080/08839514.2020.1775011 |
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