Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Siteseer: personalized navigation for the Web
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Information Retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Customer lifetime value modeling and its use for customer retention planning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Performance Monitoring of CRM Initiatives
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Mining Mobile Sequential Patterns in a Mobile Commerce Environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
User comments for news recommendation in forum-based social media
Information Sciences: an International Journal
Making the most of TV on the move: My newschannel
Information Sciences: an International Journal
Personalized recommendation of popular blog articles for mobile applications
Information Sciences: an International Journal
Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
Information Sciences: an International Journal
Effective hybrid recommendation combining users-searches correlations using tensors
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Information Sciences: an International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
The dynamic competitive recommendation algorithm in social network services
Information Sciences: an International Journal
Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
An improved CART decision tree for datasets with irrelevant feature
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems
Journal of Information Science
An implementation and evaluation of recommender systems for traveling abroad
Expert Systems with Applications: An International Journal
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
Electronic Commerce Research and Applications
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
Information Processing and Management: an International Journal
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
A quality based recommender system to disseminate information in a university digital library
Information Sciences: an International Journal
A social appraisal mechanism for online purchase decision support in the micro-blogosphere
Decision Support Systems
Defending recommender systems by influence analysis
Information Retrieval
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Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers' purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods@?purchase behavior in the current period. If a target customer's purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not utilize the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers' RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer's purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.