Performance improvement for recommender systems using ontology
Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (...
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| Format: | Article |
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Elsevier Ltd
2017
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| _version_ | 1796862656025460736 |
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| author | Bagherifard, K. Rahmani, M. Nilashi, M. Rafe, V. |
| author_facet | Bagherifard, K. Rahmani, M. Nilashi, M. Rafe, V. |
| author_sort | Bagherifard, K. |
| collection | ePrints |
| description | Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k-Nearest Neighbor (k-NN) and model-based recommendation methods. |
| first_indexed | 2024-03-05T20:14:56Z |
| format | Article |
| id | utm.eprints-77241 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:14:56Z |
| publishDate | 2017 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-772412018-05-31T09:53:36Z http://eprints.utm.my/77241/ Performance improvement for recommender systems using ontology Bagherifard, K. Rahmani, M. Nilashi, M. Rafe, V. QA75 Electronic computers. Computer science Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k-Nearest Neighbor (k-NN) and model-based recommendation methods. Elsevier Ltd 2017 Article PeerReviewed Bagherifard, K. and Rahmani, M. and Nilashi, M. and Rafe, V. (2017) Performance improvement for recommender systems using ontology. Telematics and Informatics . ISSN 0736-5853 (In Press) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028986110&doi=10.1016%2fj.tele.2017.08.008&partnerID=40&md5=3c42fbfd3b6f4952e3ff461256c5b0ed DOI:10.1016/j.tele.2017.08.008 |
| spellingShingle | QA75 Electronic computers. Computer science Bagherifard, K. Rahmani, M. Nilashi, M. Rafe, V. Performance improvement for recommender systems using ontology |
| title | Performance improvement for recommender systems using ontology |
| title_full | Performance improvement for recommender systems using ontology |
| title_fullStr | Performance improvement for recommender systems using ontology |
| title_full_unstemmed | Performance improvement for recommender systems using ontology |
| title_short | Performance improvement for recommender systems using ontology |
| title_sort | performance improvement for recommender systems using ontology |
| topic | QA75 Electronic computers. Computer science |
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