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- W2078756301 abstract "Constraint-based recommenders support users in the identification of interesting items from large and potentially complex assortments. In cases where no recommendation could be found for a given set of requirements, such systems propose recommendations that indicate minimal sets of faulty requirements. Unfortunately, such recommendations are not diversified and do not include repair proposals which triggers a low degree of satisfaction and frequent repairs of recommendation sessions. This paper presents a dynamic and diversity relaxation approach that integrates the calculation of similarity requirements of customers and recommended products resulted in each atom of customer query and the cost of each atom of customer query. The Algorithm DDn Relax returning i®at-least-n' products can be efficiently solved diversified preference of customer." @default.
- W2078756301 created "2016-06-24" @default.
- W2078756301 creator A5066599847 @default.
- W2078756301 date "2012-10-01" @default.
- W2078756301 modified "2023-10-05" @default.
- W2078756301 title "Techniques for Dynamic and Diversified Relaxation in Constraint-Based Recommender Systems" @default.
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- W2078756301 doi "https://doi.org/10.1109/bcgin.2012.153" @default.
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