A social recommender system by combining social network and sentiment similarity: A case study of healthcare

2016 ◽  
Vol 43 (5) ◽  
pp. 635-648 ◽  
Author(s):  
Donghui Yang ◽  
Chao Huang ◽  
Mingyang Wang
Keyword(s):  
Social Network ◽  
Social Context ◽  
Recommender Systems ◽  
Network Structure ◽  
Recommender System ◽  
User Preferences ◽  
Social Recommender System ◽  
Social Recommender Systems ◽  
The Social ◽  
New Framework

Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is created by analysing the micro-blog network structure. In the next step, we calculate the sentiment similarities of micro-blog users based on a sentiment feature set which is extracted from users’ tweets. Sentiment similarities are used to filter the primary recommendation list and find users who have similar attitudes on the same topic. The goal of those two steps is to make the social recommender system much more precise and to satisfy users’ psychological preferences. At the end, we use this new framework deal with big real-world data. The recommendation results of diabetes accounts of Weibo show that our method outperforms other social recommender systems.

Collaborating The Textual Reviews Of The Merchandise and Foretelling The Rating Supported Social Sentiment

2021 ◽  
pp. 63-72
Author(s):  
Vijay K ◽  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Geographic Area ◽  
Comprehensive Analysis ◽  
User Preferences ◽  
Significant Information ◽  
The Social ◽  
Prediction Technique ◽  
Considerable Length ◽  
Rating Prediction

Lately, we have seen a twist of audit sites. It presents a decent opportunity to share our experience for a considerable length of time we have bought. Be that as it may, we tend to confront the information over-burdening issue. A method for mining significant information from surveys to know a client's inclinations and produce precise proposal is fundamental. Since quite a while ago settled recommender Systems (RS) considers a few variables, similar to client's buy records, item class, and geographic area. During this work, we have proposed sentiment-based rating prediction technique (RPS) to help up the expectation precision in recommender Systems. First and foremost, we examine the social user sentimental measuring approach and calculate every user’s sentiment on things/items. Furthermore, we don't exclusively consider a client's own wistful properties anyway moreover take interpersonal social sentimental influence into study. Then, at that point, we propose to consider item name, which might be deduced by the sentimental distributions of a user set that reflect clients' comprehensive analysis. Finally, we tend to intertwine 3 factors-user sentiment similarity, interpersonal social sentimental distributions of a client opinion likeness, interpersonal social sentimental influence, associate the thing's reputation relationship into our recommender system to make a talented rating prediction. Then, at that point, we arranged a presentation analysis of the 3 sentimental factors on a genuine world dataset gathered from Yelp. Our exploratory outcomes show, the sentiment will well describe user preferences, which facilitate to hike the proposal execution.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals Formation of raiding parties for intergroup violence is mediated by social network structure

2016 ◽  
Vol 113 (43) ◽  
pp. 12114-12119 ◽  
Author(s):  
Luke Glowacki ◽  
Alexander Isakov ◽  
Richard W. Wrangham ◽  
Rose McDermott ◽  
James H. Fowler ◽  
...  
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Social Dynamics ◽  
Small Scale ◽  
East African ◽  
Nomadic Pastoralists ◽  
Social Network Structure ◽  
The Social ◽  
Total Impact ◽  
Social Network Position

Intergroup violence is common among humans worldwide. To assess how within-group social dynamics contribute to risky, between-group conflict, we conducted a 3-y longitudinal study of the formation of raiding parties among the Nyangatom, a group of East African nomadic pastoralists currently engaged in small-scale warfare. We also mapped the social network structure of potential male raiders. Here, we show that the initiation of raids depends on the presence of specific leaders who tend to participate in many raids, to have more friends, and to occupy more central positions in the network. However, despite the different structural position of raid leaders, raid participants are recruited from the whole population, not just from the direct friends of leaders. An individual’s decision to participate in a raid is strongly associated with the individual’s social network position in relation to other participants. Moreover, nonleaders have a larger total impact on raid participation than leaders, despite leaders’ greater connectivity. Thus, we find that leaders matter more for raid initiation than participant mobilization. Social networks may play a role in supporting risky collective action, amplify the emergence of raiding parties, and hence facilitate intergroup violence in small-scale societies.

The social network structure of a semi-free roaming European bison herd (Bison bonasus)

2019 ◽  
Vol 158 ◽  
pp. 97-105 ◽  
Author(s):  
Amandine Ramos ◽  
Lola Manizan ◽  
Esther Rodriguez ◽  
Yvonne J.M. Kemp ◽  
Cédric Sueur
Keyword(s):  
Social Network ◽  
Network Structure ◽  
European Bison ◽  
Bison Bonasus ◽  
Social Network Structure ◽  
The Social

scholarly journals The use of social network methodological framework in nursing care to breastfeeding women

2009 ◽  
Vol 17 (3) ◽  
pp. 354-360 ◽  
Author(s):  
Maria Helena do Nascimento Souza ◽  
Ivis Emília de Oliveira Souza ◽  
Florence Romijn Tocantins
Keyword(s):  
Social Network ◽  
Social Context ◽  
Network Analysis ◽  
Nursing Care ◽  
Nursing Practice ◽  
Methodological Framework ◽  
Strong Bond ◽  
The People ◽  
Primary Network ◽  
The Social

This study aimed to discuss the contribution of the social network methodological framework in nursing care delivered to women who breastfeed their children up to six months of age. This qualitative study aimed to elaborate the social network map of 20 women through tape-recorded interview. Social network analysis evidenced a "strong" bond between these women and members from their primary network, especially friends, neighbors, mothers or with the child's father, who were reported as the people most involved in the breastfeeding period. The contribution of this framework to nursing practice is discussed, especially in care and research processes. We believe that nurses' appropriation of this framework can be an important support for efficacious actions, as well as to favor a broader perspective on the social context people experience.

Online vrienden bepalen de overtuigingskracht van SNS-campagnes

2011 ◽  
Vol 39 (4) ◽  
Author(s):  
Guda van Noort ◽  
Marjolijn Antheunis ◽  
Eva van Reijmersdal
Keyword(s):  
Social Network ◽  
Social Context ◽  
Crucial Role ◽  
Social Network Sites ◽  
Social Connections ◽  
Social Connection ◽  
Marketing Communication ◽  
The Social

Online Friends Determine the Persuasiveness of SNS Advertising Campaigns Online Friends Determine the Persuasiveness of SNS Advertising Campaigns Marketers more and more design advertising campaigns especially for Social Network Sites (SNS), with the aim that SNS users forward these campaigns to their online network. By means of a survey, this study investigates whether the persuasiveness of such campaigns is determined by the strength of the social connection between receiver and the sender of the campaign. The results support the idea that SNS campaigns are more persuasive when forwarded by close friends, than when forwarded by less strong social connections. Thus, the social context plays a crucial role in the persuasiveness of marketing communication activities within SNS.

Toward Geospatial Collaborative Tourism Recommender Systems

2018 ◽  
pp. 212-248
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
World Wide ◽  
Information Overload ◽  
Spatial Information ◽  
User Profile ◽  
User Preferences ◽  
Research Issues ◽  
The World ◽  
Information Finding

Tourism activities are highly dependent on spatial information. Finding the most interesting travel destinations and attractions and planning a trip are still open research issues to GIScience research applied to the tourism domain. Nowadays, huge amounts of information are available over the world wide web that may be useful in planning a visit to destinations and attractions. However, it is often time consuming for a user to select the most interesting destinations and attractions and plan a trip according to his own preferences. Tourism recommender systems (TRSs) can be used to overcome this information overload problem and to propose items taking into account the user preferences. This chapter reviews related topics in tourism recommender systems including different tourism recommendation approaches and user profile representation methods applied in the tourism domain. The authors illustrate the potential of tourism recommender systems as applied to the tourism domain by the implementation of an illustrative geospatial collaborative recommender system using the Foursquare dataset.

Sign in / Sign up

Export Citation Format

Share Document