Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommender systems for evaluating computer messages
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Evaluation of web usage mining approaches for user's next request prediction
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
On a Hybrid Rule Based Recommender System
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Expert Systems with Applications: An International Journal
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Integration of heterogeneous models to predict consumer behavior
Expert Systems with Applications: An International Journal
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
Combination of Web page recommender systems
Expert Systems with Applications: An International Journal
Selecting a small number of products for effective user profiling in collaborative filtering
Expert Systems with Applications: An International Journal
Collaborative filtering with temporal dynamics
Communications of the ACM
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using quantitative association rules in collaborative filtering
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
Expert Systems with Applications: An International Journal
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Nowadays, there is a constant need for personalization in e-commerce systems. Recommender systems make suggestions and provide information about items available, however, many recommender techniques are still vulnerable to some shortcomings. In this work, we analyze how methods employed in these systems are affected by some typical drawbacks. Hence, we conduct a case study using data gathered from real recommender systems in order to investigate what machine learning methods can alleviate such drawbacks. Due to some especial features inherited by associative classifiers, we give a particular attention to this category of methods to test their capability of dealing with typical drawbacks.