A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Semantic tagging and linking of software engineering social content

Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as tag explosion, locality of tags and interpretation differences, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.     

社交网络用户行为的体裁互文性分析

社交网络是基于用户之间共同的兴趣、爱好等构建的一种社会关系网络服务。社交网络中包含了大量的用户行为,研究这些行为对增强用户体验,增加用户使用粘性,提高资源的分享率以及其它一些服务的有效性都具有十分重要的意义。体裁互文性是语言学的基本概念,它与用户行为具有一些共性特征。实验表明,社交网络用户行为和体裁互文性具有相似性,利用体裁互文性研究用户的行为将更有利于信息共享、传播以及知识通信。     

Balancing systematic and flexible exploration of social networks

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks     

Exploring social tagging for personalized community recommendations

Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users’ interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations.     

Multi-relational reinforcement for computing credibility of nodes

Nowadays, growing number of social networks are available on the internet, with which users can conveniently make friends, share information, and exchange ideas with each other. As the result, large amount of data are generated from activities of those users. Such data are regarded as valuable resources to support different mining tasks, such as predicting friends for a user, ranking users in terms of their influence on the social network, or identifying communities with common interests. Traditional algorithms for those tasks are often designed under the assumption that a user selects another user as his friend based on their common interests. As a matter of fact, users on a social network may not always develop their friends with common interest. For example, a user may randomly select other users as his friends just in order to attract more links reversely from them. Therefore, such links may not indicate his influence. In this paper, we study the user rank problem in terms of their ‘real’ influences. For this sake, common interest relationships among users are established besides their friend relationships. Then, the credible trust link from one node to another is on account of their similarities, which means the more similar the two users, the more credible their trust relation. So the credibility of a node is high if its trust inlinks are credible enough. In this work, we propose a framework that computes the credibility of nodes on a multi-relational network using reinforcement techniques. To the best of our knowledge, this is the first work to assess credibility exploited knowledge on multi-relational social networks. The experimental results on real data sets show that our framework is effective.     

Exploring and mitigating social loafing in online communities

The motivation to share members’ knowledge is critical to an online community’s survival and success. Previous research has established that knowledge sharing intentions are based on group cohesion. Several studies also suggested that social loafing behavior will seriously corrode group cohesion. Therefore, social loafing is a key obstacle to fostering online community development. Although substantial studies have been performed on the critical factors that affect social loafing in the learning group, those on online communities are still lacking. By integrating two perspectives, social capital and perceived risk, a richer understanding of social loafing behavior can be gained. In the research model, social ties and perceived risk have been driven by anonymity, offline activities, knowledge quality, and media richness. Social ties and perceived risk are hypothesized to affect social loafing in the online community, which, in turn, is hypothesized as negatively affecting group cohesion. Data collected from 323 online users in online communities provide support for the proposed model. The study shows that social loafing is a significant negative predictor of the users’ group cohesion. The study also shows that social ties and perceived risk are important components of social loafing. Anonymity, offline activities, knowledge quality, and media richness all have strong effects on social ties and perceived risk in the online community. Implications for theory and practice and suggestions for future research are discussed.     

Discovery of Web user communities and their role in personalization

One of the major innovations in personalization in the last 20?years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people??s everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users?? activity on the Web. We recall how this process has been used to model the users?? interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.     

Implicit affinity networks and social capital

Social networks are typically constructed based on explicit and well-defined relationships among individuals. In this paper, we describe another class of social networks, known as implicit affinity networks, where links are implicit in the patterns of natural affinities among individuals. An effective mathematical formulation of social capital based on implicit and explicit connections is given. Results with two Web communities, one focused on people’s interests and one focused on people’s blogs, exhibit rich dynamics and show interesting patterns of community evolution.     

Community detection in social networks using user frequent pattern mining

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.     

Recommenders in a personalized,collaborative digital library environment

We envisage an information source not only as an information resource where users may submit queries to satisfy their daily information need, but also as a collaborative working and meeting space of people sharing common interests. Indeed, we will present a highly personalized environment where not only users may organize (and search into) the information space according to their individual taste and use, but which provides advanced features of collaborative work among the users. It is up to the system to discover interesting properties about the users’ interests, relationships between users and user communities and to make recommendations based on preference patterns of the users, which is the main topic of this paper.     

How the R community creates and curates knowledge: an extended study of stack overflow and mailing lists

One of the effects of social media’s prevalence in software development is the many flourishing communities of practice where users share a common interest. These large communities use many different communication channels, but little is known about how they create, share, and curate knowledge using such channels. In this paper, we report a mixed methods study of how one community of practice, the R software development community, creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R tag in Stack Overflow and the R-Help mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple users explicitly collaborate to build knowledge, and crowdsourced, where individuals primarily work independently of each other. Moreover, we take a unique approach at slicing the data based on question score and participation activities over time. Our study reveals participation patterns, showing the existence of prolific contributors: users who are active across both channels and are responsible for a large proportion of the answers, serving as a bridge of knowledge. The key contributions of this paper are: a characterization of knowledge artifacts that are exchanged by this community of practice; the reasons why users choose one channel over the other; and insights on the community participation patterns, which indicate an evolution of the community and a shift from knowledge creation to knowledge curation.     

The social bookmark and publication management system bibsonomy

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Resource recommendation in social annotation systems: A linear-weighted hybrid approach

Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.     

An architecture and functional description to integrate social behaviour knowledge into group recommender systems

In this paper we consider the research challenges of generating a set of recommendations that will satisfy a group of users with potentially competing interests. We review different ways of combining the preferences of different users and propose an approach that takes into account the social behaviour within a group. Our method, named delegation-based prediction method, includes an analysis of the group characteristics, such as size, structure, personality of its members in conflict situations, and trust between group members. A key element in this paper is the use of social information available in the Web to make enhanced recommendations to groups. We propose a generic architecture named arise (Architecture for Recommendations Including Social Elements) and describe, as a case study, our Facebook application HappyMovie: a group recommender system that is designed to provide assistance to a group of friends that might be selecting which movie to watch on a cinema outing. We evaluate the performance (compared with the real group decision) of different recommenders that use increasing levels of social behaviour knowledge.