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25
Modeling and predicting personal information dissemination behavior
- In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Suzuki, G.; Aoki1, S.; Iwamoto1, T.; Maruyama1, D.; Koda1, T.; Kohtake1, N.; Takashio1, K.; and Tokuda1, H
, 2005
"... In this paper, we propose a new way to automatically model and predict human behavior of receiving and disseminating information by analyzing the contact and content of personal communications. A personal profile, called CommunityNet, is established for each individual based on a novel algorithm inc ..."
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Cited by 40 (3 self)
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In this paper, we propose a new way to automatically model and predict human behavior of receiving and disseminating information by analyzing the contact and content of personal communications. A personal profile, called CommunityNet, is established for each individual based on a novel algorithm incorporating contact, content, and time information simultaneously. It can be used for personal social capital management. Clusters of CommunityNets provide a view of informal networks for organization management. Our new algorithm is developed based on the combination of dynamic algorithms in the social network field and the semantic content classification methods in the natural language processing and machine learning literatures. We tested CommunityNets on the Enron Email corpus and report experimental results including filtering, prediction, and recommendation capabilities. We show that the personal behavior and intention are somewhat predictable based on these models. For instance, "to whom a person is going to send a specific email " can be predicted by one’s personal social network and content analysis. Experimental results show the prediction accuracy of the proposed adaptive algorithm is 58% better than the social network-based predictions, and is 75 % better than an aggregated model based on Latent Dirichlet Allocation with social network enhancement. Two online demo systems we developed that allow interactive exploration of CommunityNet are also discussed.
Recommending emergent teams
- In MSR ’07: Proceedings of the Fourth International Workshop on Mining Software Repositories
, 2007
"... To build successful complex software systems, develop-ers must collaborate with each other to solve issues. To facilitate this collaboration, specialized tools, such as chat and screen sharing, are being integrated into development environments. Currently, these tools require a developer to maintain ..."
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Cited by 39 (3 self)
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To build successful complex software systems, develop-ers must collaborate with each other to solve issues. To facilitate this collaboration, specialized tools, such as chat and screen sharing, are being integrated into development environments. Currently, these tools require a developer to maintain a list of other developers with whom they may wish to communicate and to determine who within this list has expertise for a specific situation. For large, dynamic projects, like several successful open-source projects, these requirements place an unreasonable burden on the devel-oper. In this paper, we show how the structure of a team emerges from how developers change software artifacts. We introduce the Emergent Expertise Locator (EEL) that uses emergent team information to propose experts to a devel-oper within their development environment as the developer works. We found that EEL produces, on average, results with higher precision and higher recall than an existing heuristic for expertise recommendation. 1
Eigen-Trend: Trend Analysis in the Blogosphere Based on Singular Value Decompositions
, 2006
"... The blogosphere---the totality of blog-related Web sites--- has become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we intro ..."
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Cited by 30 (3 self)
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The blogosphere---the totality of blog-related Web sites--- has become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we introduce a novel concept, coined eigen-trend, to represent the temporal trend in a group of blogs with common interests and propose two new techniques for extracting eigentrends in blogs. First, we propose a trend analysis technique based on the singular value decomposition. Extracted eigentrends provide new insights into multiple trends on the same keyword. Second, we propose another trend analysis technique based on a higher-order singular value decomposition. This analyzes the blogosphere as a dynamic graph structure and extracts eigen-trends that reflect the structural changes of the blogosphere over time. Experimental studies based on synthetic data sets and a real blog data set show that our new techniques can reveal a lot of interesting trend information and insights in the blogosphere that are not obtainable from traditional count-based methods.
Smallblue: Social network analysis for expertise search and collective intelligence
- In ICDE’09
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 26 (0 self)
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
On the quality of inferring interests from social neighbors
- In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10
, 2010
"... This paper intends to provide some insights of a scientific problem: how likely one’s interests can be inferred from his/her social connections – friends, friends ’ friends, 3-degree friends, etc? Is “Birds of a Feather Flocks Together ” a norm? We do not consider the friending activity on online so ..."
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Cited by 20 (1 self)
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This paper intends to provide some insights of a scientific problem: how likely one’s interests can be inferred from his/her social connections – friends, friends ’ friends, 3-degree friends, etc? Is “Birds of a Feather Flocks Together ” a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees’ time in work, and thus, provide a high quality approximation to the real social connections of employees in the
Who Can Help Me with this Change Request
- in Proc. of 17 th ICPC'09
, 2009
"... An approach to recommend a ranked list of developers to assist in performing software changes given a textual change request is presented. The approach employs a two-fold strategy. First, a technique based on information retrieval is put at work to locate the relevant units of source code, e.g., fil ..."
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Cited by 18 (6 self)
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An approach to recommend a ranked list of developers to assist in performing software changes given a textual change request is presented. The approach employs a two-fold strategy. First, a technique based on information retrieval is put at work to locate the relevant units of source code, e.g., files, classes, and methods, to a given change request. These units of source code are then fed to a technique that recommends developers based on their source code change expertise, experience, and contributions, as derived from the analysis of the previous commits. The commits are obtained from a software system’s version control repositories (e.g., Subversion). The approach is demonstrated on a bug report from KOffice, an open source application suite. 1.
Assigning change requests to software developers
, 2011
"... The paper presents an approach to recommend a ranked list of expert developers to assist in the implementation of software change requests (e.g., bug reports and feature requests). An Information Retrieval (IR)-based concept location technique is first used to locate source code entities, e.g., file ..."
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Cited by 16 (6 self)
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The paper presents an approach to recommend a ranked list of expert developers to assist in the implementation of software change requests (e.g., bug reports and feature requests). An Information Retrieval (IR)-based concept location technique is first used to locate source code entities, e.g., files and classes, relevant to a given textual description of a change request. The previous commits from version control repositories of these entities are then mined for expert developers. The role of the IR method in selectively reducing the mining space is different from previous approaches that textually index past change requests and/or commits. The approach is evaluated on change requests from three open-source systems: ArgoUML, Eclipse, andKOffice, across a range of accuracy criteria. The results show that the overall accuracies of the correctly recommended developers are between 47 and 96 % for bug reports, and between 43 and 60% for feature requests. Moreover, comparison results with two other recommendation alternatives show that the presented approach outperforms them with a substantial margin. Project leads or developers can use this approach in maintenance tasks immediately after the receipt of a change request in a free-form text.
Who Can Help Me with this Source Code Change?
"... An approach to recommend a ranked list of developers to assist in performing software changes to a particular file is presented. The ranking is based on change expertise, experience, and contributions of developers, as derived from the analysis of the previous commits involving the specific file in ..."
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Cited by 12 (5 self)
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An approach to recommend a ranked list of developers to assist in performing software changes to a particular file is presented. The ranking is based on change expertise, experience, and contributions of developers, as derived from the analysis of the previous commits involving the specific file in question. The commits are obtained from a software system’s version control repositories (e.g., Subversion). The basic premise is that a developer who has substantially contributed changes to specific files in the past is likely to best assist for their current or future change. Evaluation of the approach on a number of open source systems such as koffice, Apache
Collecting Expertise of Researchers for Finding Relevant Experts in a Peer-Review Setting. Paper presented at the 1st International ExpertFinder Workshop
- in a Peer-Review Setting. 1st International ExpertFinder Workshop
, 2007
"... We present ideas for determining the expertise of researchers across various areas of computer science and for finding relevant experts/reviewers in a peerreview setting. We explain how Semantic Web techniques for data collection and data representation using ontologies can be used in addressing thi ..."
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Cited by 11 (5 self)
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We present ideas for determining the expertise of researchers across various areas of computer science and for finding relevant experts/reviewers in a peerreview setting. We explain how Semantic Web techniques for data collection and data representation using ontologies can be used in addressing this specific “ExpertFinder ” problem. 1.
Triaging Incoming Change Requests: Bug or Commit History, or Code Authorship?
"... Abstract — There is a tremendous wealth of code authorship information available in source code. Motivated with the presence of this information, in a number of open source projects, an approach to recommend expert developers to assist with a software change request (e.g., a bug fixes or feature) is ..."
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Cited by 8 (2 self)
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Abstract — There is a tremendous wealth of code authorship information available in source code. Motivated with the presence of this information, in a number of open source projects, an approach to recommend expert developers to assist with a software change request (e.g., a bug fixes or feature) is presented. It employs a combination of an information retrieval technique and processing of the source code authorship information. The relevant source code files to the textual description of a change request are first located. The authors listed in the header comments in these files are then analyzed to arrive at a ranked list of the most suitable developers. The approach fundamentally differs from its previously reported counterparts, as it does not require software repository mining. Neither does it require training from past bugs/issues, which is often done with sophisticated techniques such as machine learning, nor mining of source code repositories, i.e., commits. An empirical study to evaluate the effectiveness of the approach on three open source systems, ArgoUML, JEdit, and MuCommander, is reported. Our approach is compared with two representative approaches: 1) using machine learning on past bug reports, and 2) based on commit logs. The presented approach is found to provide recommendation accuracies that are equivalent or better than the two compared approaches. These findings are encouraging, as it opens up a promising and orthogonal possibility of recommending developers without the need of any historical change information.
