Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Computer
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
The web beyond popularity: a really simple system for web scale RSS
Proceedings of the 15th international conference on World Wide Web
A location-aware recommender system for mobile shopping environments
Expert Systems with Applications: An International Journal
Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
An attribute-based ant colony system for adaptive learning object recommendation
Expert Systems with Applications: An International Journal
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Combination of Web page recommender systems
Expert Systems with Applications: An International Journal
A Scalable, Accurate Hybrid Recommender System
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
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With the recent tremendous increase in the volume of Web 3.0 content, content recommendation systems (CRS) have emerged as an important aspect of social network services and computing. Thus, several studies have been conducted to investigate content recommendation methods (CRM) for CRSs. However, traditional CRMs are limited in that they cannot be used in the Web 3.0 environment. In this paper, we propose a novel way to recommend high-quality web content using degree of centrality and term frequency---inverse document frequency (TF---IDF). In the proposed method, we analyze the TF---IDF and degree of centrality of collected RDF site summary and friend-of-a-friend data and then generate content recommendations based on these two analyzed values. Results from the implementation of the proposed system indicate that it provides more appropriate and reliable contents than traditional CRSs. The proposed system also reflects the importance of the role of content creators.