Content-Based Social Network User Interest Tag Extraction

Yu, Mei; Han, Xu; Gou, Xiaolu; Yu, Jian; Lv, Fang; Li, Jingyu

doi:10.14257/ijdta.2015.8.2.11

Cited by 6 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Receive_ratio.v i / represents the acceptance ratio of video v i , which is calculated as Equation (11). Reputation.v i / represents the reputation of video v i , which is calculated as Equation (12). f 1 and f 2 respectively represent the weight coefficients of the video's acceptance ratio and reputation, f 1 C f 2 D 1.…”

Section: The Quality Evaluation Of a Videomentioning

confidence: 99%

See 1 more Smart Citation

A video recommendation algorithm based on the combination of video content and social network

Cui

Dong

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Section: The Quality Evaluation Of a Videomentioning

confidence: 99%

“…Content‐based recommendation algorithm is a recommendation technology that extracts the features of content. It pays more attention to the content of the recommended item itself, and the features of content are defined by some attributes.…”

Section: Related Workmentioning

confidence: 99%

A video recommendation algorithm based on the combination of video content and social network

Cui

Dong

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…For example, content-based semantic annotation method and model-based semantic annotation method. Content-based approaches [2][3][4] mainly study how to combine the network metadata information, user comments, attention, clicks and other information during annotation stage. In contrast to the content-based algorithms, model-based algorithms [5][6][7][8][9] often use machine learning to solve the problem of semantic annotation.…”

Section: Introductionmentioning

confidence: 99%

“…Aiming to establish microblog user interest model, [2] combined clustering and classification algorithm to extract user interest tags and [5] proposed an approach of automatic document annotation with data mining algorithms: classification, clustering and named-entity recognition. [3] used content-based filtering method and distance algorithm for journal Recommendation System.…”

Section: Introductionmentioning

confidence: 99%

Random Walk-Based Semantic Annotation for On-demand Printing Products

Zhang¹,

Su²

2019

AJNNA

View full text Add to dashboard Cite

Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds "product-term" bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.

show abstract

“…In addition, some research works utilise classification method to mine users’ interests. For example, Yu et al [21] and Xu [22] combined clustering and classification algorithms to extract the tags of users’ interests. Li and Zhang [23] presented a new method using a semi-supervised reinforcement framework based on social-correlation to classify short texts and tags of users’ interests.…”

Section: Introductionmentioning

confidence: 99%

A three-layer model on users’ interests mining

Yang

Xie

Chen

2017

Journal of Information Science

View full text Add to dashboard Cite

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

show abstract

Content-Based Social Network User Interest Tag Extraction

Abstract: Abstract

Cited by 6 publications

References 10 publications

A video recommendation algorithm based on the combination of video content and social network

A video recommendation algorithm based on the combination of video content and social network

Random Walk-Based Semantic Annotation for On-demand Printing Products

A three-layer model on users’ interests mining

Contact Info

Product

Resources

About