Personalized Social Image Recommendation Method Based on User-Image-Tag Model

Zhang, Jing; Yang, Ying; Tian, Qi; Liu, Xin

doi:10.1109/tmm.2017.2701641

Cited by 41 publications

(18 citation statements)

References 30 publications

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“…RGB color space is the most commonly used color space in image processing, and the HSV color space, HIS color space and LAB color space we often use in image processing are all converted from RGB color space [19]. However, RGB has some shortcomings: it is not intuitive enough, and there is a big gap between the difference between the two colors calculated by euclide distance and the difference between the two colors actually observed by people.…”

Section: Image Recommendation Algorithm Based On Implicit Support mentioning

confidence: 99%

Image Recommendation Algorithm Based on Deep Learning

Yin

Zhang

2020

IEEE Access

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With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU-Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Then, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the lsvm-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.

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Section: Image Recommendation Algorithm Based On Implicit Support mentioning

confidence: 99%

Image Recommendation Algorithm Based on Deep Learning

Yin

Zhang

2020

IEEE Access

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“…sonalized recommendation algorithm considering image metadata information [32]. We apply its image recommendation algorithm to implement the tag recommendation.…”

Section: Personalized Social Image Recommendation (Psir): a Per-mentioning

confidence: 99%

Personalized Tag Recommendation Based on Convolution Feature and Weighted Random Walk

Liu¹,

Tianlong²,

Han³

et al. 2020

IJCIS

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Automatic image semantic annotation is of great importance for image retrieval, therefore, this paper aims to recommend tags for social images according to user preferences. With the rapid development of the image-sharing community, such as Flickr, the image resources of the social network with rich metadata information demonstrate explosive growth. How to provide semantic tagging words (also known as tag recommendation) to social images through image metadata information analysis and mining is still a question, which brings new challenges and opportunities to the semantic understanding of images. Making full use of metadata for semantic analysis of images can help to bridge the semantic gap. Thus, we propose a novel personalized tag recommendation algorithm based on the convolution feature and weighted random walk. Particularly, for a given target image, we select its visual neighbors and determine the weight of each neighbor by mining the influence of user group metadata in Flickr on image correlation, and combining group information and visual features extracted by Convolutional Neural Network (CNN). Afterwards, the weighted random walk algorithm is implemented on the neighbor-tag bipartite graph. Experimental results show that tags recommended by our proposed method can accurately describe the semantic information of images and satisfy the personalized requirements of users.

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“…Therefore, a key issue for personalized video recommendation is to obtain users interests [3]. The research of personalized recommendation has widely recognized the importance of user preference modeling [4], which aims to storage and manage user preferences for predicting a personalized interest by recording and learning user historical behaviors [5].…”

Section: Introductionmentioning

confidence: 99%

Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

Liu

et al. 2019

Applied Sciences

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With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.

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Personalized Social Image Recommendation Method Based on User-Image-Tag Model

Cited by 41 publications

References 30 publications

Image Recommendation Algorithm Based on Deep Learning

Image Recommendation Algorithm Based on Deep Learning

Personalized Tag Recommendation Based on Convolution Feature and Weighted Random Walk

Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

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