Access to massive real-time user generated personal information from micro blogging services, such as Twitter and Facebook, has the potential to enable new location-based recommendation and advertising services. However, sparse user profile information and low adoption of per-message geo-coordinate information necessitates development of location detection techniques that exposes a user's location from message content. We propose and evaluate content-based machine learning techniques to a) identify tweets containing a user's location, and, b) categorize a user location into the author's present or future location. Such an approach is advantageous because it a) relies purely on message content, b) can be used to predict a user's future presence at a location, c) relates user locations to some context (activities, trip plans, etc.), and, d) can be used to profile users constantly evolving location. Our experimental evaluation shows that the proposed techniques can identify and categorize user locations from message content with high accuracy. We also extract the time entities associated with a user's future location to show when the user would be at that location. Finally we illustrate the location-based data analytics potential of these techniques on two real-world datasets.
{"title":"Predicting time-sensitive user locations from social media","authors":"A. Jaiswal, Wei Peng, Tong Sun","doi":"10.1145/2492517.2500229","DOIUrl":"https://doi.org/10.1145/2492517.2500229","url":null,"abstract":"Access to massive real-time user generated personal information from micro blogging services, such as Twitter and Facebook, has the potential to enable new location-based recommendation and advertising services. However, sparse user profile information and low adoption of per-message geo-coordinate information necessitates development of location detection techniques that exposes a user's location from message content. We propose and evaluate content-based machine learning techniques to a) identify tweets containing a user's location, and, b) categorize a user location into the author's present or future location. Such an approach is advantageous because it a) relies purely on message content, b) can be used to predict a user's future presence at a location, c) relates user locations to some context (activities, trip plans, etc.), and, d) can be used to profile users constantly evolving location. Our experimental evaluation shows that the proposed techniques can identify and categorize user locations from message content with high accuracy. We also extract the time entities associated with a user's future location to show when the user would be at that location. Finally we illustrate the location-based data analytics potential of these techniques on two real-world datasets.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"91 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":"127041489","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}
Citation analysis is a popular area of research, which has been usually used to rank the authors and the publication venues of research papers. With huge number of publications every year, it has become difficult for the users to find relevant publication materials. One simple solution to this problem is to detect communities from the citation network and recommend papers based on the common membership in communities. But, in today's research scenario, many researchers' fields of interest spread into multiple research directions resulting in an increasing number of interdisciplinary publications. Therefore, it is necessary to detect overlapping communities for relevant recommendation. In this paper, we represent publication information as a tripartite `Publication Hypergraph' consisting of authors, papers and publication venues (conferences/journals) in three partitions. We then propose an algorithm called `OverCite', which can detect overlapping communities of authors, papers and venues simultaneously using the publication hypergraph and the citation network information. We compare OverCite with two existing overlapping community detection algorithms, Clique Percolation Method (CPM) and iLCD, applied on citation network. The experiments on a large real-world citation dataset show that OverCite outperforms other two algorithms. We also present a simple paper search and recommendation system. Based on the relevance judgements of the users, we further prove the effectiveness of OverCite over other two algorithms.
{"title":"OverCite: Finding overlapping communities in citation network","authors":"Tanmoy Chakraborty, Abhijnan Chakraborty","doi":"10.1145/2492517.2500255","DOIUrl":"https://doi.org/10.1145/2492517.2500255","url":null,"abstract":"Citation analysis is a popular area of research, which has been usually used to rank the authors and the publication venues of research papers. With huge number of publications every year, it has become difficult for the users to find relevant publication materials. One simple solution to this problem is to detect communities from the citation network and recommend papers based on the common membership in communities. But, in today's research scenario, many researchers' fields of interest spread into multiple research directions resulting in an increasing number of interdisciplinary publications. Therefore, it is necessary to detect overlapping communities for relevant recommendation. In this paper, we represent publication information as a tripartite `Publication Hypergraph' consisting of authors, papers and publication venues (conferences/journals) in three partitions. We then propose an algorithm called `OverCite', which can detect overlapping communities of authors, papers and venues simultaneously using the publication hypergraph and the citation network information. We compare OverCite with two existing overlapping community detection algorithms, Clique Percolation Method (CPM) and iLCD, applied on citation network. The experiments on a large real-world citation dataset show that OverCite outperforms other two algorithms. We also present a simple paper search and recommendation system. Based on the relevance judgements of the users, we further prove the effectiveness of OverCite over other two algorithms.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"18 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":"114196272","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}
Contact diaries are interpersonal communication logs which are obtained in sociological and epidemiological studies. These logs can be used to study the social patterns of communities over a period of time. A dataset composed of diaries maps well to a set of one-tiered, categorical, independent and egocentric networks. This paper presents an interface for visualization and analysis of contact diaries datasets using an interactive radial mapping scheme, with case studies illustrating a standard workflow using the application. We facilitate individual diary analysis, multi-dataset comparison, and an overlay interface for investigating a set of many diaries in a singular space. With this interface, network researchers can utilize visualization to enhance their analysis of contact diaries.
{"title":"An interactive visualization interface for studying egocentric, categorical, contact diary datasets","authors":"Chris Bryan, K. Ma, Yang-chih Fu","doi":"10.1145/2492517.2492636","DOIUrl":"https://doi.org/10.1145/2492517.2492636","url":null,"abstract":"Contact diaries are interpersonal communication logs which are obtained in sociological and epidemiological studies. These logs can be used to study the social patterns of communities over a period of time. A dataset composed of diaries maps well to a set of one-tiered, categorical, independent and egocentric networks. This paper presents an interface for visualization and analysis of contact diaries datasets using an interactive radial mapping scheme, with case studies illustrating a standard workflow using the application. We facilitate individual diary analysis, multi-dataset comparison, and an overlay interface for investigating a set of many diaries in a singular space. With this interface, network researchers can utilize visualization to enhance their analysis of contact diaries.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"42 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":"122477186","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 mounting pressure to enable widespread access to electronic health record systems is being felt by healthcare providers. The federal government's Meaningful Use incentives are reason alone for providers to address this significant usability issue. As the healthcare industry considers solutions, attention should be given to the Cloud and the considerable investment that has been made related to the establishment of digital identities and making them interoperable across heterogeneous systems. This research considered how the Cloud could be leveraged by healthcare providers to not only provide patients with a familiar way of accessing electronic resources but also creating a significant cost savings for providers. An examination was performed of similar work being done in other industries as well as the standards laid out by the federal government for EHRs and digital identities. This research lays out a comprehensive framework for healthcare providers to easily follow to integrate with the Cloud for identity validation, while meeting compliance guidelines for security and privacy. To demonstrate the viability of this research, a number of pilots and proof of concept projects have already been implemented at a large regional hospital and have produced immediate and tangible improvements.
{"title":"The forecast for electronic health record access: Partly cloudy","authors":"Brian Coats, Subrata Acharya","doi":"10.1145/2492517.2500329","DOIUrl":"https://doi.org/10.1145/2492517.2500329","url":null,"abstract":"The mounting pressure to enable widespread access to electronic health record systems is being felt by healthcare providers. The federal government's Meaningful Use incentives are reason alone for providers to address this significant usability issue. As the healthcare industry considers solutions, attention should be given to the Cloud and the considerable investment that has been made related to the establishment of digital identities and making them interoperable across heterogeneous systems. This research considered how the Cloud could be leveraged by healthcare providers to not only provide patients with a familiar way of accessing electronic resources but also creating a significant cost savings for providers. An examination was performed of similar work being done in other industries as well as the standards laid out by the federal government for EHRs and digital identities. This research lays out a comprehensive framework for healthcare providers to easily follow to integrate with the Cloud for identity validation, while meeting compliance guidelines for security and privacy. To demonstrate the viability of this research, a number of pilots and proof of concept projects have already been implemented at a large regional hospital and have produced immediate and tangible improvements.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"10 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":"125724716","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}
Khaled Dawoud, Shang Gao, Ala Qabaja, P. Karampelas, R. Alhajj
Information technology is advancing faster than anticipated. The amount of data captured and stored in electronic form by far exceeds the capabilities available for comprehensive analysis and effective knowledge discovery. There is always a need for new sophisticated techniques that could extract more of the knowledge hidden in the raw data collected continuously in huge repositories. Biomedicine and computational biology is one of the domains overwhelmed with huge amounts of data that should be carefully analyzed for valuable knowledge that may help uncovering many of the still unknown information related to various diseases threatening the human body. Biomarker detection is one of the areas which have received considerable attention in the research community. There are two sources of data that could be analyzed for biomarker detection, namely gene expression data and the rich literature related to the domain. Our research group has reported achievements analyzing both domains. In this paper, we concentrate on the latter domain by describing a powerful tool which is capable of extracting from the content of a repository (like PubMed) the parts related to a given specific domain like cancer, analyze the retrieved text to extract the key terms with high frequency, present the extracted terms to domain experts for selecting those most relevant to the investigated domain, retrieve from the analyzed text molecules related to the domain by considering the relevant terms, derive the network which will be analyzed to identify potential biomarkers. For the work described in this paper, we considered PubMed and extracted abstracts related to prostate and breast cancer. The reported results are promising; they demonstrate the effectiveness and applicability of the proposed approach.
{"title":"Combining information extraction and text mining for cancer biomarker detection","authors":"Khaled Dawoud, Shang Gao, Ala Qabaja, P. Karampelas, R. Alhajj","doi":"10.1145/2492517.2500281","DOIUrl":"https://doi.org/10.1145/2492517.2500281","url":null,"abstract":"Information technology is advancing faster than anticipated. The amount of data captured and stored in electronic form by far exceeds the capabilities available for comprehensive analysis and effective knowledge discovery. There is always a need for new sophisticated techniques that could extract more of the knowledge hidden in the raw data collected continuously in huge repositories. Biomedicine and computational biology is one of the domains overwhelmed with huge amounts of data that should be carefully analyzed for valuable knowledge that may help uncovering many of the still unknown information related to various diseases threatening the human body. Biomarker detection is one of the areas which have received considerable attention in the research community. There are two sources of data that could be analyzed for biomarker detection, namely gene expression data and the rich literature related to the domain. Our research group has reported achievements analyzing both domains. In this paper, we concentrate on the latter domain by describing a powerful tool which is capable of extracting from the content of a repository (like PubMed) the parts related to a given specific domain like cancer, analyze the retrieved text to extract the key terms with high frequency, present the extracted terms to domain experts for selecting those most relevant to the investigated domain, retrieve from the analyzed text molecules related to the domain by considering the relevant terms, derive the network which will be analyzed to identify potential biomarkers. For the work described in this paper, we considered PubMed and extracted abstracts related to prostate and breast cancer. The reported results are promising; they demonstrate the effectiveness and applicability of the proposed approach.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"18 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":"126552787","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}
Teng Wang, Keith C. Wang, Fredrik Erlandsson, S. F. Wu, Robert W. Faris
With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media.
{"title":"The influence of feedback with different opinions on continued user participation in online newsgroups","authors":"Teng Wang, Keith C. Wang, Fredrik Erlandsson, S. F. Wu, Robert W. Faris","doi":"10.1145/2492517.2492555","DOIUrl":"https://doi.org/10.1145/2492517.2492555","url":null,"abstract":"With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media.","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":"131239375","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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