GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Item selection by "hub-authority" profit ranking
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Efficient algorithms for incremental utility mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
A Profit-Based Business Model for Evaluating Rule Interestingness
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
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Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-commerce recommender system should focus on the profit of recommendation. This study proposes a new sequential pattern mining algorithm that incorporates the concepts of frequency, recency, and profit to discover frequent, recent, and profitable sequential patterns, called FRP-sequences. Based on the discovered sequential patterns, this study develops a collaborative recommender system to improve recommendation accuracy for customers and the profit of recommendation from the seller's perspective. The proposed recommender system clusters customers, discovers FRP-sequences for each cluster, and then recommends items to the target customers based on their frequent, recent, and profitable FRP-sequences. In the stage of discovering FRP-sequences, the transaction patterns near the current time period and profitable items are weighted more heavily to improve profit. This study uses a public food mart database to determine the performance of the proposed approach, and compares it with traditional recommendation models. The proposed system performs better than traditional recommendation models in both recommendation accuracy and profit.