Collaborative Filtering Similarity Measures: Revisiting
This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) recommender system. In addition, the author introduced some recommendations related to CF system quality improvement which should be considered in the process of formulating similarity measure that may le...
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Association for Computing Machinery
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18508/1/fskkp-2017-hael-Collaborative%20Filtering%20Similarity%20Measures%20Revisiting.pdf http://umpir.ump.edu.my/id/eprint/18508/7/fskkp-2017-hael-Collaborative%20Filtering%20Similarity1.pdf |
| _version_ | 1796992281344999424 |
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| author | Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Hujainah, Fadhl |
| author_facet | Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Hujainah, Fadhl |
| author_sort | Al-Bashiri, Hael |
| collection | UMP |
| description | This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) recommender system. In addition, the author introduced some recommendations related to CF system quality improvement which should be considered in the process of formulating similarity measure that may lead to alleviating the issue of data sparsity and some existing measures shortcomings.
Generally, CF approach is one of the most widely used and most successful methods for the recommendation system, such as e-commerce. CF system introduced items to the user based on his/her previous ratings and the ratings of his/her neighbors. Therefore, the most important stage in CF system is locating the successful neighbor. Nevertheless, the sparsity of data is the major issue faced
by the memory-based CF. The reason behind this is that many of the users rated a few number of items from the huge number of available items. This has encouraged many researchers to provide solutions. One of these solutions was by proposing or updating similarities measures take in considerations the global information
preference, all ratings provided by users, the size of common ratings, and so on. In this work, the researcher discussed these measures alongside with their limitations. In addition, the researcher also listed some advicesthat are important in the process of locating successful neighbors, which may help researchers to improve the quality of CF system. |
| first_indexed | 2024-03-06T12:17:44Z |
| format | Conference or Workshop Item |
| id | UMPir18508 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:17:44Z |
| publishDate | 2017 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | UMPir185082019-10-18T02:32:56Z http://umpir.ump.edu.my/id/eprint/18508/ Collaborative Filtering Similarity Measures: Revisiting Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Hujainah, Fadhl QA76 Computer software This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) recommender system. In addition, the author introduced some recommendations related to CF system quality improvement which should be considered in the process of formulating similarity measure that may lead to alleviating the issue of data sparsity and some existing measures shortcomings. Generally, CF approach is one of the most widely used and most successful methods for the recommendation system, such as e-commerce. CF system introduced items to the user based on his/her previous ratings and the ratings of his/her neighbors. Therefore, the most important stage in CF system is locating the successful neighbor. Nevertheless, the sparsity of data is the major issue faced by the memory-based CF. The reason behind this is that many of the users rated a few number of items from the huge number of available items. This has encouraged many researchers to provide solutions. One of these solutions was by proposing or updating similarities measures take in considerations the global information preference, all ratings provided by users, the size of common ratings, and so on. In this work, the researcher discussed these measures alongside with their limitations. In addition, the researcher also listed some advicesthat are important in the process of locating successful neighbors, which may help researchers to improve the quality of CF system. Association for Computing Machinery 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18508/1/fskkp-2017-hael-Collaborative%20Filtering%20Similarity%20Measures%20Revisiting.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18508/7/fskkp-2017-hael-Collaborative%20Filtering%20Similarity1.pdf Al-Bashiri, Hael and Abdulgabber, Mansoor Abdullateef and Awanis, Romli and Hujainah, Fadhl (2017) Collaborative Filtering Similarity Measures: Revisiting. In: International Conference on Advances in Image Processing (ICAIP 2017) , 25-27 August 2017 , Bangkok, Thailand. pp. 1-5.. ISBN 978-1-4503-5295-6 |
| spellingShingle | QA76 Computer software Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Hujainah, Fadhl Collaborative Filtering Similarity Measures: Revisiting |
| title | Collaborative Filtering Similarity Measures: Revisiting |
| title_full | Collaborative Filtering Similarity Measures: Revisiting |
| title_fullStr | Collaborative Filtering Similarity Measures: Revisiting |
| title_full_unstemmed | Collaborative Filtering Similarity Measures: Revisiting |
| title_short | Collaborative Filtering Similarity Measures: Revisiting |
| title_sort | collaborative filtering similarity measures revisiting |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/18508/1/fskkp-2017-hael-Collaborative%20Filtering%20Similarity%20Measures%20Revisiting.pdf http://umpir.ump.edu.my/id/eprint/18508/7/fskkp-2017-hael-Collaborative%20Filtering%20Similarity1.pdf |
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