The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Blogs, Wikis and Official Statistics: New Perspectives on the Use of Web 2.0 by Statistical Offices

This paper explains the roles that blogs, wikis and social networking play in the provision and dissemination of official statistics.Official statistics, internet, web

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook

Exploring the Use of Social Bookmarking Technology in Education: An Analysis of Students’ Experiences using a Course-specific Delicious.com Account

With more than 4.6 million people, mostly undergraduates, enrolling in at least one online course in fall of 2008, students are showing that they are comfortable with the concept of technology in education. Many students in online classes, however still have to deal with the high cost of textbooks and supplemental materials. Online technologies, however, can provide other alternatives to costly coursepacks and textbooks. Faculty and students may be able to replace or supplement coursepacks and textbooks with social bookmarking sites. This study shows how social bookmarking, specifically Delicious.com, can be used in a course to provide an inexpensive answer to the question of rising course materials costs. Through a series of online focus groups, 53 students enrolled in a “Social Media and Public Relations” course revealed their apprehension toward using an unknown technology and discussed their positive and negative experiences with using the course-specific Delicious.com account. Implications for how social bookmarking can impact online and offline learning are discussed

Web information search and sharing :

制度:新 ; 報告番号:甲2735号 ; 学位の種類:博士(人間科学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新493

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Media Usage in Post-Secondary Education and Implications for Teaching and Learning

The Web 2.0 has permeated academic life. The use of online information services in post-secondary education has led to dramatic changes in faculty teaching methods as well as in the learning and study behavior of students. At the same time, traditional information media, such as textbooks and printed handouts, still form the basic pillars of teaching and learning. This paper reports the results of a survey about media usage in teaching and learning conducted with Western University students and instructors, highlighting trends in the usage of new and traditional media in higher education by instructors and students. In addition, the survey comprises part of an international research program in which 20 universities from 10 countries are currently participating. Further, the study will hopefully become a part of the ongoing discussion of practices and policies that purport to advance the effective use of media in teaching and learning