Personalized Social Image Tag Recommendation Algorithm Based on Tensor Decomposition

2021 ◽  
Author(s):  
Shangzheng Liu ◽  
Bin Liu
Keyword(s):  
Tensor Decomposition ◽  
Social Image ◽  
Recommendation Algorithm ◽  
Tag Recommendation

scholarly journals Hybrid Feature based Classification of Images using Supervised Methods for Tag Recommendation

2020 ◽  
Vol 9 (11) ◽  
pp. 135-138
Keyword(s):  
Wavelet Packet ◽  
Social Image ◽  
Tag Recommendation ◽  
Image Sharing ◽  
Other Information ◽  
Feature Based ◽  
Supervised Methods ◽  
Classification Of Images ◽  
Flickr Images

Recent advancement in digital technology and vast use of social image sharing websites leads to a huge database of images. On social websites the images are associated with the tags or keywords which describe the visual content of the images and other information. Theses tags are used by social image sharing websites for retrieval of the images. Therefore, it is important to assign appropriate tags to the images. To assign related tags, it is necessary to choose appropriate classifier for automatic classification of images into various sematic categories with respect to the classification accuracy which is important step for image tag recommendation. In this paper, three supervised classifier algorithms are implemented for image classifications which are SVM, kNN and random forest and its performance is analyzed on Flickr images. For classification of images, the features are extracted using color moment and wavelet packet descriptor

Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

2017 ◽  
Vol 44 (3) ◽  
pp. 298-313 ◽  
Author(s):  
Hyun-Ki Hong ◽  
Gun-Woo Kim ◽  
Dong-Ho Lee
Keyword(s):  
Multimedia Content ◽  
Ranking Algorithm ◽  
Digital Cameras ◽  
Social Image ◽  
Tag Recommendation ◽  
Image Sharing ◽  
Social Multimedia ◽  
Similar Images ◽  
Smart Mobile

The volumes of multimedia content and users have increased on social multimedia sites due to the prevalence of smart mobile devices and digital cameras. It is common for users to take pictures and upload them to image-sharing websites using their smartphones. However, the tag characteristics deteriorate the quality of tag-based image retrieval and decrease the reliability of social multimedia sites. In this article, we propose a semantic tag recommendation technique exploiting associated words that are semantically similar or related to each other using the interwiki links of Wikipedia. First, we generate a word relationship graph after extracting meaningful words from each article in Wikipedia. The candidate words are then rearranged according to importance by applying a link-based ranking algorithm and then the top-k words are defined as the associated words for the article. When a user uploads an image, we collect visually similar images from a social image database. After propagating the proper tags from the collected images, we recommend associated words related to the candidate tags. Our experimental results show that the proposed method can improve the accuracy by up to 14% compared with other works and that exploiting associated words makes it possible to perform semantic tag recommendation.

Personalized Recommendation Based on Contextual Awareness and Tensor Decomposition

2018 ◽  
Vol 16 (3) ◽  
pp. 39-51
Author(s):  
Zhenjiao Liu ◽  
Xinhua Wang ◽  
Tianlai Li ◽  
Lei Guo
Keyword(s):  
Recommendation System ◽  
Tensor Decomposition ◽  
Personalized Recommendation ◽  
Third Order ◽  
Recommendation Algorithm ◽  
Singular Value Decomposition Method ◽  
Contextual Awareness ◽  
Value Decomposition ◽  
Item Item

In order to solve users' rating sparsely problem existing in present recommender systems, this article proposes a personalized recommendation algorithm based on contextual awareness and tensor decomposition. Via this algorithm, it was first constructed two third-order tensors to represent six types of entities, including the user-user-item contexts and the item-item-user contexts. And then, this article uses a high order singular value decomposition method to mine the potential semantic association of the two third-order tensors above. Finally, the resulting tensors were combined to reach the recommendation list to respond the users' personalized query requests. Experimental results show that the proposed algorithm can effectively improve the effectiveness of the recommendation system. Especially in the case of sparse data, it can significantly improve the quality of the recommendation.

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