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Social learning enables learners to interact with one another, which is a critical way to foster creativity. Although online social networking technologies have been widely adopted, few empirical studies have been conducted to illustrate the effect of online social interactions on creativity in the context of learning. In this research-in-progress, we attempt to fill this gap by investigating how...
This paper presents a highly parallel solution for cross-document co reference resolution, which can deal with billions of documents that exist in the current web. At the core of our solution lies a novel algorithm for community detection in large scale graphs. We operate on graphs which we construct by representing documents' keywords as nodes and the colocation of those keywords in a document as...
Building upon the expectation disconfirmation theory and prior satisfaction studies, this study proposes a research model explaining user information satisfaction in knowledge-based virtual communities. The proposed model was tested using an online survey that was conducted among the users of a non-profit-making Bulletin Board Systems established by a local university in mainland China. The results...
Intercultural collaboration facilitated by machine translation has gradually spread in various settings. Still, little is known as for the practice of machine-translation mediated communication. This paper investigates how machine translation affects intercultural communication in practice. Based on communication in which multilingual communication system is applied, we identify four communication...
Community Context-attribute-centric Collaborative Information Environment (CCCIE) enables provision of services to users with same interests, by employing time awareness and demand-oriented perspective to transform one to one (1-1), one dimensional location based service paradigm to one to many (1-N), n-dimensional situation-aware community services. Unlike conventional Location Based Services (LBS)...
When foraging for information, users face a tradeoff between the accuracy and value of the acquired information and the time spent collecting it, a problem which also surfaces when seeking answers to a question posed to a large community. We empirically study how people behave when facing these conflicting objectives using data from Yahoo Answers, a community driven question-and-answer site. We first...
Risk is associated with almost every activity that is done in trust computing. Analyzing the Risk involved in a trust computing is important to decide whether to proceed with the transaction or not. In this paper, SMT, a trust computing mechanism is proposed for large-scale P2P systems based on risk evaluating properties. SMT incorporates two risk evaluating properties to reflect the dynamic trust...
Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content. The main contribution of FIRSt is an integrated strategy that enables...
Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems. Algorithms of this type derive personalized product propositions for customers by exploiting statistics derived from vast amounts of transaction data.Traditionally, basic CF algorithms have exploited a single category of ratings despite the fact that on many platforms a variety of different...
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