An Improved Product Recommendation Method for Collaborative Filtering
Collaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9129689/ |
| _version_ | 1818617245598744576 |
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| author | Arta Iftikhar Mustansar Ali Ghazanfar Mubbashir Ayub Zahid Mehmood Muazzam Maqsood |
| author_facet | Arta Iftikhar Mustansar Ali Ghazanfar Mubbashir Ayub Zahid Mehmood Muazzam Maqsood |
| author_sort | Arta Iftikhar |
| collection | DOAJ |
| description | Collaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix. Based on the computed similarity, a prediction is made for the unknown or new product. There are many similarity computation methods, such as the Pearson correlation coefficient (PCC), Jaccard, Mean square difference, Cosine, etc. however, the accuracy of product recommendations using these methods is not very promising. This work introduces an improved product recommendation method for collaborative filtering, which is based on the triangle similarity. However, the downside of triangle similarity is that it only considers the common ratings of users. The proposed similarity measure not only focuses on common ratings but also consider the ratings of those items that are not commonly rated from pairs of users. The obtained similarity is further complemented with the user rating preference (URP) behavior in giving rating preferences. To evaluate the accuracy, experiments are performed on the six commonly used datasets in the field of CF. Experimental results prove that the proposed similarity measure performs well as compared to the existing similarity measures. |
| first_indexed | 2024-12-16T17:02:38Z |
| format | Article |
| id | doaj.art-05114306c89f49178e81cccf863a9007 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:02:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-05114306c89f49178e81cccf863a90072022-12-21T22:23:41ZengIEEEIEEE Access2169-35362020-01-01812384112385710.1109/ACCESS.2020.30059539129689An Improved Product Recommendation Method for Collaborative FilteringArta Iftikhar0Mustansar Ali Ghazanfar1Mubbashir Ayub2https://orcid.org/0000-0002-4772-4343Zahid Mehmood3https://orcid.org/0000-0003-4888-2594Muazzam Maqsood4https://orcid.org/0000-0002-2709-0849Department of Software Engineering, University of Engineering and Technology Taxila, Taxila, PakistanDepartment of Software Engineering, University of Engineering and Technology Taxila, Taxila, PakistanDepartment of Software Engineering, University of Engineering and Technology Taxila, Taxila, PakistanDepartment of Computer Engineering, University of Engineering and Technology Taxila, Taxila, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanCollaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix. Based on the computed similarity, a prediction is made for the unknown or new product. There are many similarity computation methods, such as the Pearson correlation coefficient (PCC), Jaccard, Mean square difference, Cosine, etc. however, the accuracy of product recommendations using these methods is not very promising. This work introduces an improved product recommendation method for collaborative filtering, which is based on the triangle similarity. However, the downside of triangle similarity is that it only considers the common ratings of users. The proposed similarity measure not only focuses on common ratings but also consider the ratings of those items that are not commonly rated from pairs of users. The obtained similarity is further complemented with the user rating preference (URP) behavior in giving rating preferences. To evaluate the accuracy, experiments are performed on the six commonly used datasets in the field of CF. Experimental results prove that the proposed similarity measure performs well as compared to the existing similarity measures.https://ieeexplore.ieee.org/document/9129689/Collaborative filteringrecommender systemstriangle similarityuser preferences |
| spellingShingle | Arta Iftikhar Mustansar Ali Ghazanfar Mubbashir Ayub Zahid Mehmood Muazzam Maqsood An Improved Product Recommendation Method for Collaborative Filtering IEEE Access Collaborative filtering recommender systems triangle similarity user preferences |
| title | An Improved Product Recommendation Method for Collaborative Filtering |
| title_full | An Improved Product Recommendation Method for Collaborative Filtering |
| title_fullStr | An Improved Product Recommendation Method for Collaborative Filtering |
| title_full_unstemmed | An Improved Product Recommendation Method for Collaborative Filtering |
| title_short | An Improved Product Recommendation Method for Collaborative Filtering |
| title_sort | improved product recommendation method for collaborative filtering |
| topic | Collaborative filtering recommender systems triangle similarity user preferences |
| url | https://ieeexplore.ieee.org/document/9129689/ |
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