GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
The Journal of Machine Learning Research
A Music Recommendation System with a Dynamic K-means Clustering Algorithm
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A recommender system with interest-drifting
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using Wikipedia to boost collaborative filtering techniques
Proceedings of the fifth ACM conference on Recommender systems
Extended information inference model for unsupervised categorization of web short texts
Journal of Information Science
SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems
Journal of Information Science
A probability-based unified framework for semantic search and recommendation
Journal of Information Science
Scope of ontological annotation in e-commerce
International Journal of Business Information Systems
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Collaborative filtering, which is a popular approach for developing recommendation systems, exploits the exact match of items that users have accessed. If the users access different items, they are considered as unlike-minded users even though they may actually be semantically like-minded. To solve this problem, we propose a semantic collaborative filtering model that represents the semantics of usersâ聙聶 preferences and items with their corresponding concepts. In this work, we extend the Bayesian belief network (BBN)-based model because it provides a clear formalism for representing usersâ聙聶 preferences and items with concepts. Because the conventional BBN-based model regards the index terms derived from items as concepts, it does not exploit domain knowledge. We have therefore extended this conventional model to exploit concepts derived from domain knowledge. A practical approach to exploiting domain knowledge is to use world knowledge such as the Open Directory Project web directory or the Wikipedia encyclopaedia. Through experiments, we show that our model outperforms other conventional collaborative filtering models while comparing the recommendation quality when using different world knowledge.