Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic and cyber domains. For example, acts of terrorism and organized crime are considered one of the greatest threats to national security. In the UK alone, the social and economic costs associated with organized crime are estimated between £20 and £40 billion per year (NCA, 2011:4). Threats to national security, such as that against critical infrastructures not only stem from man-made acts but also from natural hazards. Katrina (2005), Fukushima (2011) and Hurricane Sandy (2012) are examples that highlight the vulnerability of critical infrastructures to natural hazards and the crippling effect they have on the social and economic wellbeing of a community and a nation. With this dynamic and complex threat landscape, network analysis has emerged as a key enabler in supporting defence and security. With the advent of `big data' and increasing processing power, network analysis can reveal insights with regards to structural and dynamic properties thereby facilitating greater understanding of complex networks, their entities, interdependencies and vulnerabilities. This poster paper introduces relevant theoretical frameworks and applications of network analysis in support of the defence and security domain. This paper reflects the body of contributions by leading researchers to an upcoming book entitled: Networks and Network Analysis for Defence and Security, Springer Publishing.
{"title":"Networks and network analysis for defence and security","authors":"A. Masys","doi":"10.1145/2492517.2492602","DOIUrl":"https://doi.org/10.1145/2492517.2492602","url":null,"abstract":"Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic and cyber domains. For example, acts of terrorism and organized crime are considered one of the greatest threats to national security. In the UK alone, the social and economic costs associated with organized crime are estimated between £20 and £40 billion per year (NCA, 2011:4). Threats to national security, such as that against critical infrastructures not only stem from man-made acts but also from natural hazards. Katrina (2005), Fukushima (2011) and Hurricane Sandy (2012) are examples that highlight the vulnerability of critical infrastructures to natural hazards and the crippling effect they have on the social and economic wellbeing of a community and a nation. With this dynamic and complex threat landscape, network analysis has emerged as a key enabler in supporting defence and security. With the advent of `big data' and increasing processing power, network analysis can reveal insights with regards to structural and dynamic properties thereby facilitating greater understanding of complex networks, their entities, interdependencies and vulnerabilities. This poster paper introduces relevant theoretical frameworks and applications of network analysis in support of the defence and security domain. This paper reflects the body of contributions by leading researchers to an upcoming book entitled: Networks and Network Analysis for Defence and Security, Springer Publishing.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as `Video Games' and `Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.
{"title":"Chelsea won, and you bought a T-shirt: Characterizing the interplay between Twitter and e-commerce","authors":"Haipeng Zhang, Nish Parikh, Gyanit Singh, Neel Sundaresan","doi":"10.1145/2492517.2500302","DOIUrl":"https://doi.org/10.1145/2492517.2500302","url":null,"abstract":"The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as `Video Games' and `Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Berlingerio, Danai Koutra, Tina Eliassi-Rad, C. Faloutsos
Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.
{"title":"Network similarity via multiple social theories","authors":"M. Berlingerio, Danai Koutra, Tina Eliassi-Rad, C. Faloutsos","doi":"10.1145/2492517.2492582","DOIUrl":"https://doi.org/10.1145/2492517.2492582","url":null,"abstract":"Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122191596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique.
{"title":"Lab-on-a-Chip turns soft: Computer-aided, software-enabled microfluidics design","authors":"A. K. Soe, M. Fielding, S. Nahavandi","doi":"10.1145/2492517.2500230","DOIUrl":"https://doi.org/10.1145/2492517.2500230","url":null,"abstract":"The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130517991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Semenov, Alexander G. Nikolaev, J. Veijalainen
This paper describes traces of user activity around a alleged online social network profile of a Boston Marathon bombing suspect, after the tragedy occurred. The analyzed data, collected with the help of an automatic social media monitoring software, includes the perpetrator's page saved at the time the bombing suspects' names were made public, and the subsequently appearing comments left on that page by other users. The analyses suggest that a timely protection of online media records of a criminal could help prevent a large-scale public spread of communication exchange pertaining to the suspects/criminals' ideas, messages, and connections.
{"title":"Online activity traces around a “Boston bomber”","authors":"Alexander Semenov, Alexander G. Nikolaev, J. Veijalainen","doi":"10.1145/2492517.2500316","DOIUrl":"https://doi.org/10.1145/2492517.2500316","url":null,"abstract":"This paper describes traces of user activity around a alleged online social network profile of a Boston Marathon bombing suspect, after the tragedy occurred. The analyzed data, collected with the help of an automatic social media monitoring software, includes the perpetrator's page saved at the time the bombing suspects' names were made public, and the subsequently appearing comments left on that page by other users. The analyses suggest that a timely protection of online media records of a criminal could help prevent a large-scale public spread of communication exchange pertaining to the suspects/criminals' ideas, messages, and connections.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116789256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to compensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.
{"title":"Compensatory seeding in networks with varying avaliability of nodes","authors":"Jarosław Jankowski, Radosław Michalski, Przemyslaw Kazienko","doi":"10.1145/2492517.2500256","DOIUrl":"https://doi.org/10.1145/2492517.2500256","url":null,"abstract":"Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to compensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132171026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tae Sato, Masanori Fujita, Minoru Kobayashi, Koji Ito
We propose a recommendation method that considers the user's individual preference and influence from other users in social media. This method predicts the user's individual preference and influence from other users by applying the probability of divergence from random-selection based on a statistical hypothesis test as a form of modified content-based filtering. We evaluated the proposed method by focusing on the rate at which items that have recommended tags are contained among all items. The proposed method is shown to have higher accuracy than traditional content-based filtering. It is especially effective when some percentage of the items have recommendation tags.
{"title":"Recommender system by grasping individual preference and influence from other users","authors":"Tae Sato, Masanori Fujita, Minoru Kobayashi, Koji Ito","doi":"10.1145/2492517.2500283","DOIUrl":"https://doi.org/10.1145/2492517.2500283","url":null,"abstract":"We propose a recommendation method that considers the user's individual preference and influence from other users in social media. This method predicts the user's individual preference and influence from other users by applying the probability of divergence from random-selection based on a statistical hypothesis test as a form of modified content-based filtering. We evaluated the proposed method by focusing on the rate at which items that have recommended tags are contained among all items. The proposed method is shown to have higher accuracy than traditional content-based filtering. It is especially effective when some percentage of the items have recommendation tags.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124192025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christoph Scholz, M. Atzmüller, Mark Kibanov, Gerd Stumme
Understanding the process of link creation is rather important for link prediction in social networks. Therefore, this paper analyzes contact structures in networks of face-to-face spatial proximity, and presents new insights on the dynamic and static contact behavior in such real world networks. We focus on face-to-face contact networks collected at different conferences using the social conference guidance system Conferator. Specifically, we investigate the strength of ties and its connection to triadic closures in face-to-face proximity networks. Furthermore, we analyze the predictability of all, new and recurring links at different points of time during the conference. In addition, we consider network dynamics for the prediction of new links.
{"title":"How do people link? Analysis of contact structures in human face-to-face proximity networks","authors":"Christoph Scholz, M. Atzmüller, Mark Kibanov, Gerd Stumme","doi":"10.1145/2492517.2492521","DOIUrl":"https://doi.org/10.1145/2492517.2492521","url":null,"abstract":"Understanding the process of link creation is rather important for link prediction in social networks. Therefore, this paper analyzes contact structures in networks of face-to-face spatial proximity, and presents new insights on the dynamic and static contact behavior in such real world networks. We focus on face-to-face contact networks collected at different conferences using the social conference guidance system Conferator. Specifically, we investigate the strength of ties and its connection to triadic closures in face-to-face proximity networks. Furthermore, we analyze the predictability of all, new and recurring links at different points of time during the conference. In addition, we consider network dynamics for the prediction of new links.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers have capitalized on microblogging services, such as Twitter, for detecting and monitoring real world events. Existing approaches have based their conclusions on data collected by monitoring a set of pre-defined keywords. In this paper, we show that this manner of data collection risks losing a significant amount of relevant information. We then propose an adaptive crawling model that detects emerging popular hashtags, and monitors them to retrieve greater amounts of highly associated data for events of interest. The proposed model analyzes the traffic patterns of the hashtags collected from the live stream to update subsequent collection queries. To evaluate this adaptive crawling model, we apply it to a dataset collected during the 2012 London Olympic Games. Our analysis shows that adaptive crawling based on the proposed Refined Keyword Adaptation algorithm collects a more comprehensive dataset than pre-defined keyword crawling, while only introducing a minimum amount of noise.
{"title":"Exploiting hashtags for adaptive microblog crawling","authors":"Xinyue Wang, L. Tokarchuk, F. Cuadrado, S. Poslad","doi":"10.1145/2492517.2492624","DOIUrl":"https://doi.org/10.1145/2492517.2492624","url":null,"abstract":"Researchers have capitalized on microblogging services, such as Twitter, for detecting and monitoring real world events. Existing approaches have based their conclusions on data collected by monitoring a set of pre-defined keywords. In this paper, we show that this manner of data collection risks losing a significant amount of relevant information. We then propose an adaptive crawling model that detects emerging popular hashtags, and monitors them to retrieve greater amounts of highly associated data for events of interest. The proposed model analyzes the traffic patterns of the hashtags collected from the live stream to update subsequent collection queries. To evaluate this adaptive crawling model, we apply it to a dataset collected during the 2012 London Olympic Games. Our analysis shows that adaptive crawling based on the proposed Refined Keyword Adaptation algorithm collects a more comprehensive dataset than pre-defined keyword crawling, while only introducing a minimum amount of noise.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116810319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sogol Naseri, Arash Bahrehmand, Chen Ding, Chi-Hung Chi
Recently, researchers have taken tremendous strides in attempting to synthesize conventional social judgments and automated filtering within recommender systems. In this study, we aim to enhance recommendation efficiency via integrating social networking information with traditional recommendation algorithms. To achieve this objective, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendship and membership, in measuring the closeness of two users. Subsequently, we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm show some positive results that attest the efficiency of our proposed approach.
{"title":"Enhancing tag-based collaborative filtering via integrated social networking information","authors":"Sogol Naseri, Arash Bahrehmand, Chen Ding, Chi-Hung Chi","doi":"10.1145/2492517.2492658","DOIUrl":"https://doi.org/10.1145/2492517.2492658","url":null,"abstract":"Recently, researchers have taken tremendous strides in attempting to synthesize conventional social judgments and automated filtering within recommender systems. In this study, we aim to enhance recommendation efficiency via integrating social networking information with traditional recommendation algorithms. To achieve this objective, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendship and membership, in measuring the closeness of two users. Subsequently, we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm show some positive results that attest the efficiency of our proposed approach.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116824350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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