Erratum to “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 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.

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Joint Matrix Factorization: A Novel Approach for Recommender System

IEEE Access ◽

10.1109/access.2020.3044046 ◽

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

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Movie Recommendation through Multiple Bias Analysis

Applied Sciences ◽

10.3390/app11062817 ◽

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.

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