Low-Rank Multi-View Embedding Learning for Micro-Video Popularity Prediction

Jing, Peiguang; Su, Yuting; Nie, Liqiang; Bai, Xu; Liu, Jing; Wang, Meng

doi:10.1109/tkde.2017.2785784

Cited by 111 publications

(39 citation statements)

References 51 publications

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“…Popularity of online contents and user-generated-contents including news, products, and Youtube videos have been analyzed and predicted in [33,35,37,38,49]. Along with the improvement of SNSs, social popularity prediction of the posted images [3,7,16,17,25,28,29,44,48,51] and micro-videos [2,6,19] are focused by both academics and industries. Most of these popularity prediction are conducted by data-driven feature-based learning phases, and some researches embedded them with temporal models representing the variations on time information [31,38,48].…”

Section: Social Popularity Predictionmentioning

confidence: 99%

User-Aware Folk Popularity Rank

Wang

Zhang

Yamasaki

2019

Proceedings of the 27th ACM International Conference on Multimedia

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In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.

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Section: Social Popularity Predictionmentioning

confidence: 99%

User-Aware Folk Popularity Rank

Wang

Zhang

Yamasaki

2019

Proceedings of the 27th ACM International Conference on Multimedia

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“…Ting-Chia Hsu et al [5] provide a recommendation system for video clips based on the ID3 algorithm using the Python programming language. Peiguang Jing et al [1] presents a transductive low-rank multi-view regression (TLRMVR) framework for micro-video popularity prediction. By taking advantages of low-rank representation and multi-view learning all heterogeneous features extracted from different views into a common feature subspace and achieved enhanced robust feature representation for regression analysis by using an effective optimization algorithm.…”

Section: Literature Survey Collaborative Filtering Recommendationmentioning

confidence: 99%

“…Video is a short time video, which lasts for 30 seconds to 300 seconds. The short time micro-videos are popular with young people, because the teenagers prefer to watch the micro-video [1] [4][6] on their comfortable time through mobile devices. For micro-video producers, the problem is they do not know how many people like their products, and do not know how many times their video have been watched.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Micro Video Recommendation System for Content Writer

Girish¹

2019

IJRASET

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With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngsters. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Recommendation systems play an important role in helping Micro Video system. Micro-videos afford many potential opportunities ranging from network content caching to online advertising. Micro-videos are created by individuals and contain some unique characteristics, including egocentric and self-facing views. Micro Video Recommendation System for Content writer.

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“…In other words, the compressed sensing method can reconstruct the original signal accurately by using a small number of linear combinations of the compression transform coefficients. As a promising method, the theory has been introduced to image reconstruction [4][5][6][7], wireless sensor networks [8][9][10], speech coding [11,12], image memorability prediction [13], and others [14][15][16]. Its broad scope and generality has made the CS method become a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction

Yang

Qin

2018

Mathematical Problems in Engineering

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Magnetic resonance imaging (MRI) has become a helpful technique and developed rapidly in clinical medicine and diagnosis. Magnetic resonance (MR) images can display more clearly soft tissue structures and are important for doctors to diagnose diseases. However, the long acquisition and transformation time of MR images may limit their application in clinical diagnosis. Compressed sensing methods have been widely used in faithfully reconstructing MR images and greatly shorten the scanning and transforming time. In this paper we present a compressed sensing model based on median filter for MR image reconstruction. By combining a total variation term, a median filter term, and a data fitting term together, we first propose a minimization problem for image reconstruction. The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results. One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation. We then apply the split Bregman technique for fast minimization and give an efficient algorithm. Finally, we apply our method to numbers of MR images and compare it with a related method. Reconstruction results and comparisons demonstrate the accuracy and efficiency of the proposed model.

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Low-Rank Multi-View Embedding Learning for Micro-Video Popularity Prediction

Cited by 111 publications

References 51 publications

User-Aware Folk Popularity Rank

User-Aware Folk Popularity Rank

A Survey on Micro Video Recommendation System for Content Writer

Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction

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