Frequency-based similarity measure for multimedia recommender systems
Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Springer-Verlag
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/43318 |
| _version_ | 1848756657825251328 |
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| author | Rehman, Zia ur Hussain, Farookh Khadeer Hussain, Omar |
| author_facet | Rehman, Zia ur Hussain, Farookh Khadeer Hussain, Omar |
| author_sort | Rehman, Zia ur |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed. |
| first_indexed | 2025-11-14T09:15:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-43318 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:15:41Z |
| publishDate | 2012 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-433182017-09-13T15:52:28Z Frequency-based similarity measure for multimedia recommender systems Rehman, Zia ur Hussain, Farookh Khadeer Hussain, Omar multimedia content recommender systems personalization similarity measures collaborative filtering Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed. 2012 Journal Article http://hdl.handle.net/20.500.11937/43318 10.1007/s00530-012-0281-1 Springer-Verlag restricted |
| spellingShingle | multimedia content recommender systems personalization similarity measures collaborative filtering Rehman, Zia ur Hussain, Farookh Khadeer Hussain, Omar Frequency-based similarity measure for multimedia recommender systems |
| title | Frequency-based similarity measure for multimedia recommender systems |
| title_full | Frequency-based similarity measure for multimedia recommender systems |
| title_fullStr | Frequency-based similarity measure for multimedia recommender systems |
| title_full_unstemmed | Frequency-based similarity measure for multimedia recommender systems |
| title_short | Frequency-based similarity measure for multimedia recommender systems |
| title_sort | frequency-based similarity measure for multimedia recommender systems |
| topic | multimedia content recommender systems personalization similarity measures collaborative filtering |
| url | http://hdl.handle.net/20.500.11937/43318 |