Ranking and combining social network data for web personalization

Abstract

Various Web-based social network data reflect user interests from multiple perspectives in a distributed environment. They need to be integrated for better user modelling and personalized services. We argue that in different scenarios, different social networks play different roles and their degrees of importance are not equivalent. Hence, ranking strategies among different social network data sources are needed. In addition, combining different social network data can produce interesting subsets of these data with different levels of importance. In this paper, we propose social network data ranking and composition strategies, we validate the proposed methods by collaboration network data (Semantic Web Dog Food) and micro-blogging data (from Twitter), then we use the ranked and composed results for developing a Web-based personalized academic visit recommendation system to show their potential effectiveness.

Topic Sensitive_Social Relation Rank Algorithm for Efficient Social Search

“…최근 들어 이러한 인간 관계망은 관계망이 어떻게 형성되고 해체되는가와 형성된 관계망을 어떻 게 유지하고 있는가에 대한 관계망 분석의 측면에서 핵심적인 연구가 이루어지고 있다 [1][2] . 즉, 이러한 인 관 관계망에 근간을 둔 소셜 네트워크 [1] 는 웹 환경에 [8] , Topic_Sensitive PageRank 방법 [9] , 1) www.friendster.com 2) www.orkut.com 두 방법을 결합한 방법 [10] 등이 있다.…”

An Approach to Ranking the Sources of Information Dissemination in Social Networks

“…In fact, this subject is "media" (between). In the work presented in paper [27], a strategy for ranking information sources by user interests was proposed. Thus, the work took into account the range of interests of the user.…”