Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2014 ◽  
Vol 11 (4) ◽  
pp. 32-46 ◽  
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Individual Preference ◽  
Online Information ◽  
Information Discovery ◽  
Interpersonal Closeness ◽  
Probabilistic Matrix Factorization ◽  
Recommendation Accuracy ◽  
Improved Performance ◽  
Interest Similarity

Recommender systems (RS) have been widely employed to suggest personalized online information to simplify user's information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this paper, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics that user is explicitly and implicitly interested in. Concretely, to further enhance recommendation accuracy, four social factors, individual preference, interpersonal trust influence, interpersonal interest similarity and interpersonal closeness degree, are simultaneously injected into our recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, the authors infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance (accuracy and diversity) over the existing models in which social information have not been fully considered.

Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2019 ◽  
pp. 132-154
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen ◽  
Baohua Qiang
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Online Information ◽  
Information Discovery ◽  
Proposed Model ◽  
Probabilistic Matrix Factorization ◽  
Series Of Experiments ◽  
Networks Analysis ◽  
Recommendation Accuracy ◽  
Improved Performance

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

Alike people, alike interests? Inferring interest similarity in online social networks

2015 ◽  
Vol 69 ◽  
pp. 92-106 ◽  
Author(s):  
Xiao Han ◽  
Leye Wang ◽  
Noel Crespi ◽  
Soochang Park ◽  
Ángel Cuevas
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Interest Similarity

scholarly journals Recommendation as Collaboration in Web Search

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 35-45 ◽  
Author(s):  
Barry Smyth ◽  
Jill Freyne ◽  
Maurice Coyle ◽  
Peter Briggs
Keyword(s):  
Recommender Systems ◽  
Search Engines ◽  
Web Search ◽  
Online Information ◽  
Information Discovery ◽  
Proactive Approach ◽  
Systems Research ◽  
Traditional Approaches ◽  
New Generation

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

scholarly journals Recommendation Based on Trust Diffusion Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jinfeng Yuan ◽  
Li Li
Keyword(s):  
Matrix Factorization ◽  
High Performance ◽  
Recommendation System ◽  
Online Information ◽  
Trust Network ◽  
Data Sparsity ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Sparsity Problem ◽  
Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

scholarly journals Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis

2021 ◽  
Author(s):  
William J. Brady ◽  
Jay Joseph Van Bavel
Keyword(s):  
Social Media ◽  
Effect Size ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Meta Analysis ◽  
Online Information ◽  
Political Messages ◽  
Social Lives ◽  
Social Activists ◽  
Social Media Platforms

Over 4 billion people now use social media platforms. As our social lives become more entangled than ever before with online social networks, it is important to understand the dynamics of online information diffusion. This is particularly true for the political domain, as political elites, disinformation profiteers and social activists all utilize social media to gain influence by spreading information. Recent work found that emotional expressions related to the domain of morality (moral emotion expression) are associated with increased diffusion of political messages--a phenomenon we called ‘moral contagion’. Here, we perform a large, pre-registered direct replication (N = 849,266) of Brady et al. (2017), as well as a meta-analysis of all available data testing moral contagion (5 independent labs, 27 studies, N = 4,821,006). The estimate of moral contagion in the available population of studies is positive and significant (IRR = 1.12, 95% CI = [1.06, 1.19]), such that each message is 12% more likely to be shared for each additional moral-emotional word. The mean effect size of the large, pre-registered replication (IRR = 1.15) better estimated the effect size of the available population of studies than the original study (IRR = 1.20). These findings reinforce the importance of replication and producing a pre-registered analysis to generate accurate estimates of effect size for future studies.

A Study on Online Social Networks Theme Semantic Computing Model

2016 ◽  
Vol 13 (4) ◽  
pp. 67-90
Author(s):  
Chen Fu ◽  
Xu Yuemei ◽  
Ni Yihan
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Great Influence ◽  
Semantic Distance ◽  
Spreading Rate ◽  
Online Information ◽  
Network Information ◽  
Huge Impact ◽  
Semantic Computing

The widespread use of Mobile Intelligent Terminals and ubiquitous access to networks has enabled online information sources including Weibo and Wechat to bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. The highly real-time content, fission-like spreading rate and enormous public opinion guiding forces created in this process will cast great influence on the society. Thus, semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text, the calculation and comparison of semantic distance, the classification of nodes and the discovery of community can be realized. In this article, semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies. Then, huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward in this article. In the end, outlooks for future jobs are provided.

scholarly journals Mutual Influence of Users Credibility and News Spreading in Online Social Networks

2021 ◽  
Vol 13 (5) ◽  
pp. 107
Author(s):  
Vincenza Carchiolo ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni ◽  
Marialaura Previti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Mutual Influence ◽  
Collective Intelligence ◽  
Online Information ◽  
Fake News ◽  
Potential Growth ◽  
Multiplex Network ◽  
Time News ◽  
Information Providers

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

Recommender System with Composite Social Trust Networks

2016 ◽  
Vol 13 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Chaochao Chen ◽  
Xiaolin Zheng ◽  
Mengying Zhu ◽  
Litao Xiao
Keyword(s):  
Online Social Networks ◽  
Prediction Accuracy ◽  
Social Trust ◽  
Convergence Speed ◽  
Trust Network ◽  
True Value ◽  
Trust Networks ◽  
Factorization Model ◽  
Implicit Trust ◽  
Probabilistic Matrix Factorization

The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

scholarly journals The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

2020 ◽  
Author(s):  
Ford Lumban Gaol ◽  
Tokuro Matsuo
Keyword(s):  
Big Data ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Large Scale ◽  
Information Overload ◽  
Social Trust ◽  
Recommendation Systems ◽  
Large Scale Data ◽  
Social Big Data ◽  
Recommendation Accuracy

Abstract Introduction : Social Big data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in Social Big data. If any user intends to select product such as movies, books etc. from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Case Description : Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity but not efficient for scalability. Discussion and Evaluation: The main focus of this paper is to improve scalability and provide better recommendations to users with large-scale data in less response time. We have partitioned social big graph and distributed it on different nodes based on Mahout and PowerGraph like system. Conclusion : In our approach, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using our approach. In our proposed approach ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets proves that better locality and recommendation accuracy is achieved by using our proposed approach.

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