Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Learning to Refine Indexing by Introspective Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Collaborative Filtering Data in Case-Based Recommendation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Supporting travel decision making through personalized recommendation
Designing personalized user experiences in eCommerce
Proceedings of the 10th international conference on Intelligent user interfaces
Adaptive recommendation: putting the best foot forward
ISICT '04 Proceedings of the 2004 international symposium on Information and communication technologies
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Navigation support for learners in informal learning environments
Proceedings of the 2008 ACM conference on Recommender systems
A Value Supplementation Method for Case Bases with Incomplete Information
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Explicit vs implicit profiling: a case-study in electronic programme guides
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Trust no one: evaluating trust-based filtering for recommenders
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
The adaptive web
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Smallworlds: visualizing social recommendations
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
LinkedVis: exploring social and semantic career recommendations
Proceedings of the 2013 international conference on Intelligent user interfaces
A case-based solution to the cold-start problem in group recommenders
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Data Mining, or Knowledge Discovery as it is also known, is becoming increasingly useful in a wide variety of applications. In the following paper, we lookat its use in combating some of the traditional issues faced with recommender systems. We discuss our ongoing work which aims to enhance the performance of PTV, an applied recommender system working in the TV listings domain. This system currently combines the results of separate user-based collaborative and case-based components to recommend programs to users. Our extension to this idea operates on the theory of developing a case-based view of the collaborative component itself. By using data mining techniques to extract relationships between programme items, we can address the sparsity/maintenance problem. We also adopt a unique approach to recommendation ranking which combines user similarities and item similarities to provide more effective recommendation orderings. Experimental results corroborate our ideas, demonstrating the effectiveness of data mining in improving recommender systems by providing similarity knowledge to address sparsity, both at user-based recommendation level and recommendation ranking level.