Abstract
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.
Highlights
Multimedia streaming capable devices and social networks are growing rapidly
We focus the discussion on two key components: (1) available bandwidth and visual quality estimation on the user’s NScreen devices and (2) components of the recommendation algorithm, namely, the network and video quality aware similarity, program genres-based similarity, and the joint matrix factorization model to jointly factorize these similarities with the rating matrix
This paper presents a novel N-Screen content recommender system that ensures a visual quality on user’s N-Screen devices and effective access network available bandwidth utilization with his content preferences
Summary
Multimedia streaming capable devices and social networks are growing rapidly. The convergence of broadband access networks, multimedia-capable devices, and social networks provide a platform to access multimedia content anytime and anywhere and to share the content and opinions with friends and other users. CBF needs structural information of both the experienced and available programs These methods do not consider the users opinion about contents and assume that similar programs are rated . CBF has the limitation of a structural information requirement, useful for text-based recommendations but ineffective for unstructured items such as movies and music [6] These filtering techniques restrict the user to only those types of programs that user previously experienced and cannot recommend programs with different features. We propose a novel recommender system that considers user access network heterogeneous nature of available bandwidth and visual quality on user N-Screen devices, in order to ensure that user device and access network are capable of streaming and displaying the content.
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