Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Ρ-Queries: enabling querying for semantic associations on the semantic web
WWW '03 Proceedings of the 12th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
A multilayer ontology-based hybrid recommendation model
AI Communications - Recommender Systems
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
Wireless Personal Communications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Design and Implementation of a Fixed-Mobile Convergent Music Search Engine (FMC-MUSE)
Wireless Personal Communications: An International Journal
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Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user's preferences. After acquiring users' preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a real-world case, a movie-recommender system adopting proposed recommendation strategies, is implemented.