Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks

Xiong, Xi; Qiao, Shaojie; Han, Nan; Xiong, Fei; Bu, Zhan; Li, Ronghua; Yue, Kun; Yuan, Guan

doi:10.1016/j.neucom.2019.09.060

Cited by 34 publications

(20 citation statements)

References 13 publications

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“…where e k h represents the influence of k th head attentional output h →k u on the node when calculating the aggregation expression of node u. h →k u is the output of node u through the k th head attention, and it can be calculated by (11). Vectors z u and h →k u are multiplied by the Shared weight vector W h and then concatenated as the input of the single-layer feedforward neural network; b h is the bias.…”

Section: Social Domainmentioning

confidence: 99%

“…On the other hand, the widespread existence of social media has dramatically enriched the social activities of network users and produced productive social relationships; the integration of these social relationships has improved the quality of the recommender system and solved the cold-start problem. In recent years, researchers have proposed a large number of recommender systems [11,12] based on social networks that homogenize social relationships. However, it may restrain user representation learning in each respective domain, since users behave and interact differently in two domains, which makes their representations heterogeneous [13].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Xiong

et al. 2020

Complexity

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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.

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Section: Social Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhao

Xiong

et al. 2020

Complexity

Self Cite

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“…[68]- [70]. The mobility and spatial distribution of vector, as well as the host population may also be examined by using point of interest based community formation [71], [72].…”

Section: Discussionmentioning

confidence: 99%

Impact of Seasonal Conditions on Vector-Borne Epidemiological Dynamics

2020

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Vector-borne diseases such as malaria, dengue fever, West Nile virus, and so forth are some of the most prominent threats to human health. They are transmitted to the human population by infected insects or by direct transmission between humans. The epidemic process relies on suitable environmental and climatic conditions. Indeed, climatic factors affect the development of pathogens in vectors as well as the population dynamics of the vectors impacting significantly the incidence of disease in the human population. While the influence of the climatic conditions on Vector-borne diseases is well-documented, there is a strong need to design more realistic epidemiological models incorporating environmental features that show a close relationship with the epidemic process observed in the human population. Indeed, classical models concentrate either on the climatic influence on the vector population dynamics or on the connectivity patterns of the host population, loosing the full picture of the epidemic process dynamics. Inspired by real data of infectious diseases, a Seasonal Susceptible-Infected-Recovered (Seasonal SIR) epidemiological model is developed and analyzed. The proposed model incorporates the influence of the temperature variations together with the heterogeneous structure of the human interaction network on the spreading process of vector-borne diseases. Simulations are performed in order to get a better understanding of the climate variations and of the heterogeneous nature of the contact network on the transmission dynamics. Results show that failing to incorporate these features on the model can lead to a poor estimation of the maximum fraction of infected individuals in the host population. Furthermore there is a serious influence on the time needed to reach this maximum. The Seasonal SIR model proves to finely model the dynamics of outbreaks observed in real-world situations. It provides a basis for more effective predictions of disease outbreaks that can be used in order to implement appropriate control measures to contain epidemics. INDEX TERMS Vector-borne diseases, epidemics, heterogeneous contact network, climate variations, dynamics on networks, SIR model, epidemiological models.

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“…Zhang et al introduced a susceptibleinfected-true-removed model of rumour spreading to account for members in a network who know or can discern the truth [28]. 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].…”

Section: Literature Reviewmentioning

confidence: 99%

The Spread of Information in Virtual Communities

Zhang

Meng

et al. 2020

Complexity

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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.

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Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks

Cited by 34 publications

References 13 publications

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

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

Impact of Seasonal Conditions on Vector-Borne Epidemiological Dynamics

The Spread of Information in Virtual Communities

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