scholarly journals A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Farman Ullah ◽  
Ghulam Sarwar ◽  
Sungchang Lee
Keyword(s):  
Recommender System ◽  
Matrix Factorization ◽  
Access Network ◽  
Visual Quality ◽  
User Preferences ◽  
Access Networks ◽  
Available Bandwidth ◽  
Network Bandwidth ◽  
Screen Content ◽  
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 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.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals Joint Matrix Factorization: A Novel Approach for Recommender System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 224596-224607
Author(s):  
Shaolun Sun ◽  
Yuetong Xiao ◽  
Yue Huang ◽  
Sen Zhang ◽  
Heng Zheng ◽  
...  
Keyword(s):  
Recommender System ◽  
Matrix Factorization ◽  
Novel Approach ◽  
Joint Matrix Factorization

scholarly journals N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Farman Ullah ◽  
Ghulam Sarwar ◽  
Sungchang Lee
Keyword(s):  
Recommender System ◽  
Access Network ◽  
User Preferences ◽  
Usage Pattern ◽  
Hybrid Framework ◽  
Multiple Devices ◽  
Hybrid Recommender System ◽  
Pattern Information ◽  
Hybrid Recommender ◽  
User Device

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.

Cloud-integrated wireless-optical broadband access network with survivability

2020 ◽  
Vol 10 ◽  
Author(s):  
Sangita Solanki ◽  
Raksha Upadhyay ◽  
Uma Rathore Bhatt
Keyword(s):  
Access Network ◽  
Access Networks ◽  
Manhattan Distance ◽  
Broadband Access ◽  
Optical Access Networks ◽  
Component Level ◽  
Backup Path ◽  
Wireless Router ◽  
Path Computation ◽  
Broadband Access Network

Cloud-integrated wireless optical broadband (CIW) access networks inheriting advantages of cloud computing, wireless and optical access networks have a broad prospect in the future. Due to failure of components like OLT level, ONU level, link or path failure and cloud component level in CIW, survivability is becoming one of the important issues. In this paper, we have presented cloud-integrated wireless-optical broadband access network with survivability using integer linear programming (ILP) model, to minimize the number of cloud components while providing maximum backup paths. Hence, we have proposed protection through cloud-integrated wireless router to available ONUs (PCIWRAO). So, evaluated the backup path computation. We have considered ONU level failure in which the affected traffic is transferred through wireless routers and cloud component to the available ONUs using Manhattan distance algorithm. Simulation results show different configurations for different number of routers and cloud components illustrating available backup path when ONU fails.

scholarly journals Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fu Jie Tey ◽  
Tin-Yu Wu ◽  
Chiao-Ling Lin ◽  
Jiann-Liang Chen
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Mobile Networks ◽  
Reference Value ◽  
User Preferences ◽  
Digital Information ◽  
Internet Applications ◽  
Social Media Networks ◽  
Recommendation Accuracy ◽  
Gain Access

AbstractRecent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

scholarly journals Cooperation and information replication in wireless networks

2016 ◽  
Vol 374 (2062) ◽  
pp. 20150123 ◽  
Author(s):  
Konstantinos Poularakis ◽  
Leandros Tassiulas
Keyword(s):  
Network Performance ◽  
Access Network ◽  
Network Architectures ◽  
Backbone Networks ◽  
Network Bandwidth ◽  
Multicast Service ◽  
Radio Access ◽  
Maximum Benefit ◽  
Wireless Medium ◽  
Cell Tower

A significant portion of today's network traffic is due to recurring downloads of a few popular contents. It has been observed that replicating the latter in caches installed at network edges—close to users—can drastically reduce network bandwidth usage and improve content access delay. Such caching architectures are gaining increasing interest in recent years as a way of dealing with the explosive traffic growth, fuelled further by the downward slope in storage space price. In this work, we provide an overview of caching with a particular emphasis on emerging network architectures that enable caching at the radio access network. In this context, novel challenges arise due to the broadcast nature of the wireless medium, which allows simultaneously serving multiple users tuned into a multicast stream, and the mobility of the users who may be frequently handed off from one cell tower to another. Existing results indicate that caching at the wireless edge has a great potential in removing bottlenecks on the wired backbone networks. Taking into consideration the schedule of multicast service and mobility profiles is crucial to extract maximum benefit in network performance.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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