MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
A study on the applications of data mining techniques to enhance customer lifetime value
WSEAS Transactions on Information Science and Applications
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
The adaptive web
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Group recommender systems generate a set of recommendations that will satisfy a group of customers with potentially competing purchase interests. This paper proposes a research and operational model which effectively enhances Group Recommender Model to boost the customer purchases. For this purpose, it uses the communication and collaboration of two major sources namely Mobile Money Operator and Outlet. MMO proactively monitors the spending pattern of the customers who make purchases using their mobile money. Outlet performs customer segmentation based on RFM (Recency, Frequency and Monetary) score after which a Recursive Cluster Elimination is performed that eliminates customers within the targeted segment. Recursive Frequent Item set Mining and Recursive Market Basket Analysis are performed for the rest of customers in the targeted segment. From the obtained results, the product preferences of the remaining customers in the segment are identified based on which offers are formulated and recommended for the entire segment. It is then communicated to the MMO that intimates these offers to the potential customers among the segment. This model results in boosting customer purchases, expanding customer base and effects in the profitability of the combined source.