Dynamic discovery of favorite locations in spatio-temporal social networks

Self Cite

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

Section: Introductionmentioning

confidence: 99%

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Zhao

Xiong

et al. 2020

Self Cite

“…Some of our former works have been applied in the area of extracting reliable information in the social networks. Xiong et al [28,29] made effective suggestion of location despite a large amount of rumor and noise. A study by Xiong et al [30] helps to recognize the real information from web data.…”

Section: Related Workmentioning

confidence: 99%

Statistical Analysis of Dispelling Rumors on Sina Weibo

Deng

Wen

et al. 2020

Self Cite

Analyzing the process and results of dispelling rumors is a prerequisite for designing an effective anti-rumor strategy. Current research on this subject focuses on the simulation experiments, short of empirical study. By using the False Information Publicity Results of Sina Weibo as the data source of empirical research, this article compares the typical features of rumor and anti-rumor accounts. Furthermore, taking COVID-19 as the target topic, distributions of the reported time, frequency, platform penalty levels, and diffusion parameters of rumors related to COVID-19 are given, and some interesting results are obtained.

“…Xiong et al introduced the location concept to a local social network model [29] and further extended it to the recommendation system via information spreading in a local-based social network [30,31]. In their recent research, Xiong et al combined the location and temporal effects of a social network, proposing constructive advice on dynamic management [32]. Zhang et al studied networks that can be subdivided into smaller groups called communities and proposed a node-ranking algorithm called AI Rank using two factors: attractive power (which measures the number of followers a node has compared to its neighbours) and initiating power (which accounts for the communities that a node's neighbours belong to) [33].…”

Section: Literature Reviewmentioning

confidence: 99%

The Spread of Information in Virtual Communities

Zhang

Meng

et al. 2020

With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.