Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two char...Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two characteristics： social relations and an ask-reply mechanism. As users＇ behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers＇ concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named ＂Quora＂, and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users＇ ability to offer prompt and reliable help, taking into account activity, authority and social reputation char- acteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm. Experimental results show the efficiency of the proposed method for ranking potential answer-providers.展开更多
Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, ...Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, optimization and integration. A prominent application is a viral marketing campaign which aims to use a small number of targeted infl uence users to initiate cascades of infl uence that create a global increase in product adoption. In this paper, we analyze mainly evaluation methods of user infl uence based on IDM evaluation model, Page Rank evaluation model, use behavior model and some other popular influence evaluation models in currently social network. And then, we extract the core idea of these models to build our influence evaluation model from two aspects, relationship and activity. Finally, the proposed approach was validated on real world datasets,and the result of experiments shows that our method is both effective and stable.展开更多
Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)f...Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)for Microblog network. Specifically,a PSO-based algorithm is developed to learn the user influence,where not only the number of followers is incorporated,but also the interactions among users(e.g.,forwarding and commenting on other users' tweets). Three social factors,the influence and the activity of the target user,together with the coherence between users,are fused to improve the performance of proposed recommendation strategy. Experimental results show that,compared to the well-known Page Rank-based algorithm,the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect.展开更多
基金supported by the Fundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China under Grant No.61271041+2 种基金the National Basic Research Program of China (973 Program) under Grant No.2009CB320504the iCore Integrated Project under Grant No.287708the National Scienceand Technology Major Project under Grants No.2012ZX03005008-001,No.2012ZX03002008
文摘Community Question Answering （CQA） websites have greatly facilitated users＇ lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newlymerged community features two characteristics： social relations and an ask-reply mechanism. As users＇ behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers＇ concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named ＂Quora＂, and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users＇ ability to offer prompt and reliable help, taking into account activity, authority and social reputation char- acteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm. Experimental results show the efficiency of the proposed method for ranking potential answer-providers.
基金supported by the Research Fund for the Doctoral Program(New Teachers)Ministry of Education of China under Grant No.20121103120032+2 种基金Humanity and Social Science Youth foundation of Ministry of Education of China under Grant No.13YJCZH065General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China under Grant No.km201410005012Open Research Fund of Beijing Key Laboratory of Trusted Computing,Open Research Fund of Key Laboratory of Trustworthy Distributed Computing and Service(BUPT),Ministry of Education
文摘Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, optimization and integration. A prominent application is a viral marketing campaign which aims to use a small number of targeted infl uence users to initiate cascades of infl uence that create a global increase in product adoption. In this paper, we analyze mainly evaluation methods of user infl uence based on IDM evaluation model, Page Rank evaluation model, use behavior model and some other popular influence evaluation models in currently social network. And then, we extract the core idea of these models to build our influence evaluation model from two aspects, relationship and activity. Finally, the proposed approach was validated on real world datasets,and the result of experiments shows that our method is both effective and stable.
基金supported by National Natural Science Foundation of China(No.61171109)Applied Basic Research Programs of Sichuan Science and Technology Department(No.2014JY0215)Basic Research Plan in SWUST(No.13zx9101)
文摘Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)for Microblog network. Specifically,a PSO-based algorithm is developed to learn the user influence,where not only the number of followers is incorporated,but also the interactions among users(e.g.,forwarding and commenting on other users' tweets). Three social factors,the influence and the activity of the target user,together with the coherence between users,are fused to improve the performance of proposed recommendation strategy. Experimental results show that,compared to the well-known Page Rank-based algorithm,the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect.
