Fab: content-based, collaborative recommendation
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Machine Learning
Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8
Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Collaborative Learning and Recommender Systems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Knowledge and Information Systems
Bayesian web document classification through optimizing association word
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Context-based recommendation service in ubiquitous commerce
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
As ubiquitous commerce is coming, the ubiquitous recommendation systems utilize collaborative filtering to help users with fast searches for the best suitable items by analyzing the similar preference. However, collaborative filtering may not provide high quality recommendation because it does not consider user's preference on the attribute, the first rater problem, and the sparsity problem. This paper proposes the user preference through learning user profile for ubiquitous recommendation systems to solve the current problems. In addition, to determine the similarity between the users belonging to particular categories and new users, we assign different statistical values to the preference through learning user profile. We evaluated the proposed method on the EachMovie dataset and it was found to significantly outperform the previously proposed method.