A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization
We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (qual...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/806517 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563849413263360 |
|---|---|
| author | Farman Ullah Ghulam Sarwar Sungchang Lee |
| author_facet | Farman Ullah Ghulam Sarwar Sungchang Lee |
| author_sort | Farman Ullah |
| collection | DOAJ |
| description | We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user’s N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user’s N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues. |
| format | Article |
| id | doaj-art-77454fe3e76c4dfc92aaeed1e244e32d |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-77454fe3e76c4dfc92aaeed1e244e32d2025-02-03T01:12:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/806517806517A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix FactorizationFarman Ullah0Ghulam Sarwar1Sungchang Lee2Department of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of KoreaDepartment of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of KoreaDepartment of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of KoreaWe propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user’s N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user’s N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues.http://dx.doi.org/10.1155/2014/806517 |
| spellingShingle | Farman Ullah Ghulam Sarwar Sungchang Lee A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization The Scientific World Journal |
| title | A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization |
| title_full | A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization |
| title_fullStr | A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization |
| title_full_unstemmed | A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization |
| title_short | A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization |
| title_sort | network and visual quality aware n screen content recommender system using joint matrix factorization |
| url | http://dx.doi.org/10.1155/2014/806517 |
| work_keys_str_mv | AT farmanullah anetworkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization AT ghulamsarwar anetworkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization AT sungchanglee anetworkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization AT farmanullah networkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization AT ghulamsarwar networkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization AT sungchanglee networkandvisualqualityawarenscreencontentrecommendersystemusingjointmatrixfactorization |