Fab: content-based, collaborative recommendation
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Communications of the ACM
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Ontology Based Personalized Search
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Personalization Management Systems: Minitrack Introduction
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation
ACM Transactions on Internet Technology (TOIT)
PeRES: a personalized recommendation education system based on multi-agents & SCORM
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
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Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback – poor accuracy of recommendation on one-and-only items – because most of them do not understand the item’s semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques.