Mining association rules between sets of items in large databases
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WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
A new method for similarity indexing of market basket data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
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A Taxonomy of Recommender Agents on theInternet
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IEEE Internet Computing
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Solving bicriteria 0-1 knapsack problems using a labeling algorithm
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Evaluating collaborative filtering recommender systems
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SNACK: incorporating social network information in automated collaborative filtering
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Algorithms for mining association rules in bag databases
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Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
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A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Solving efficiently the 0-1 multi-objective knapsack problem
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Business-to-business electronic market place selection
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A multi-agent-based model for a negotiation support system in electronic commerce
Enterprise Information Systems
Electronic marketplace definition and classification: literature review and clarifications
Enterprise Information Systems
Pattern Recognition, Fourth Edition
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Fast, effective heuristics for the 0-1 multi-dimensional knapsack problem
Computers and Operations Research
A mathematical model for product selection strategies in a recommender system
Expert Systems with Applications: An International Journal
Enterprise Information Systems
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Expert Systems with Applications: An International Journal
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
PB-ADVISOR: A private banking multi-investment portfolio advisor
Information Sciences: an International Journal
A flexible framework for context-aware recommendations in the social commerce domain
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Electronic commerce (EC) has become an important support for business and is regarded as an efficient system that connects suppliers with online users. Among the applications of EC, a recommender system (RS) is undoubtedly a popular issue to make the best recommendation to the users. Even if many approaches have been proposed to perfect the recommendation, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation scheme, and an output interface of recommendations in the RS is still lacking. Besides, the fundamental issue of profit consideration for an EC company is not stressed in general terms. Therefore, this study aims to construct an RS with a strategy-oriented operation module regarding the above aspects; and with this module, an approach named clique-effects collaborative filtering (CECF) for predicting the consumer's purchase behavior was proposed. Finally, we applied our proposed module to a 3C retailer in Taiwan, and promising results were obtained. Scope and Purpose: This study aims to construct a comprehensive module for the recommender systems. The proposed strategy-oriented operation module comprises the essential parts of a recommender system. By utilizing the proposed module with marketing strategies and an effective on-line interface scheme, the recommender system could emphasize not only the customer's satisfaction as conventional recommender system suggested, but also the supplier's profit which shall be an important issue to an E-commerce company. Thus, a better recommendation environment could be displayed.