Ghulam Sarwar

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 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.

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 (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on users 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 users 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.

QoS and QoE Aware N-Screen Multicast Service

The paper focuses on ensuring the quality-of-service (QoS) and quality-of-experience (QoE) requirements of users having heterogeneous devices in a multicast session. QoS parameters such as bit rate, delays, and packet losses are good indicators for optimizing network services but fall short in characterizing user perception (QoE). In N-Screen service, the users have different devices with heterogeneous attributes like screen size, resolution, and access network interface, and the users have different QoE on N-Screen devices with the same QoS parameters. We formulate the objective function of the N-Screen multicast grouping to ensure the minimum user’s QoE with smaller bandwidth requirement. We propose a dynamic user reassignment scheme to maintain and satisfy the QoE by adapting the user’s membership to the varying network conditions. The proposed schemes combine the available bandwidth and multimedia visual quality to ensure the QoS and QoE. In the network architecture, we introduce the functions of the QoS and QoE aware multicast group management and the estimation schemes for the QoS and QoE parameters. The simulation results show that the proposed multicast service ensures the network QoS and guarantees the QoE of users in the varying network conditions.

Hierarchical control structure for provisioning N-screen services in mobile operator network

In this paper, we propose hierarchical control architecture and request routing mechanisms to support N-Screen services and mobility of both sources and devices in heterogeneous networks. N-Screen services enable users to manage their active sessions among multiple devices having different capabilities, access networks and locations. To support N-Screen services in mobile operator networks, we introduce new functions that maintain the list of active devices with their sessions and network parameters for efficient N-Screen session management. The system supports the terminal and personal mobility of both the sources and devices without affecting the overall multicast connections and minimizes the service disruption. We propose a hierarchical structure for request routing and content sharing that tries to add a user to an active session locally first. The system offloads the backhaul and core networks traffic by localized session adaptation and sharing and improves the scalability issue of existing centralized mobile network architecture. We focus on the physical placement of the new nodes and mechanisms for request routing. Simulation results show that the use of hierarchical adaptation nodes in mobile operator networks can significantly reduce network latency and traffic, and improve the system throughput.

N-Screen Smart TV contents personalization system using adaptive hybrid similarity approach

In this paper, we propose N-Screen Smart TV contents personalization system. The N-Screen Smart TV provides contents to user multiple devices having different Codec, bit rate and display format at the different location and time. The proposed system considers user N-Screen device attributes and current access network speed for visual perceptual quality on the user device, and ensure that user device is capable to display with his preferences. Furthermore, we present a novel similarity method using user rating variation to improve the accuracy of the collaborative filtering. In addition, we suggest a weight-based adaptive hybrid similarity to combine collaborative and content-based filtering. The proposed system ensures that current access network is capable to stream and recommended contents have high visual quality on the user device, and improve the accuracy, cold start issue and sparsity.

Session Management and Control Architecture for N-Screen Services

Abstract In this paper, we propose a session management and control architecture for N-Screen services, which enable users to change devices and transfer contents among user’s devices during service by session transfer and split. In N-Screen services, users may have multiple devices with different attribute such as screen resolution, CPU capability and access network interfaces. Also, since users may change devices during service, or one user may use multiple stream, N-Screen services need to enable the user to share and transfer contents across N-Screen devices. We introduce the management and control servers to provide session split over user multiple devices and session continuity while changing device. Furthermore, the proposed architecture provides the device capabilities aware session continuity. In addition, the proposed scheme minimizes the session transfer delay and content server processing load. We present results that show the effectiveness and usefulness of proposed architecture.

Scalable Hybrid Recommender System with Temporal Information

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.