N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering
This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation se...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/679849 |
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| _version_ | 1849690138834108416 |
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| author | Farman Ullah Ghulam Sarwar Sungchang Lee |
| author_facet | Farman Ullah Ghulam Sarwar Sungchang Lee |
| author_sort | Farman Ullah |
| collection | DOAJ |
| description | This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware
recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements. |
| format | Article |
| id | doaj-art-ec79c0a64e0e4a64bfcc5e72fc9b9f38 |
| institution | DOAJ |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ec79c0a64e0e4a64bfcc5e72fc9b9f382025-08-20T03:21:24ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/679849679849N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace ClusteringFarman 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 KoreaThis paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.http://dx.doi.org/10.1155/2014/679849 |
| spellingShingle | Farman Ullah Ghulam Sarwar Sungchang Lee N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering The Scientific World Journal |
| title | N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering |
| title_full | N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering |
| title_fullStr | N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering |
| title_full_unstemmed | N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering |
| title_short | N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering |
| title_sort | n screen aware multicriteria hybrid recommender system using weight based subspace clustering |
| url | http://dx.doi.org/10.1155/2014/679849 |
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