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- W2394075573 abstract "Recommendation systems have been widely used in many fields such as e-commerce.The cold-start problem is one of difficulties on recommendation systems.This paper designed a cold-start recommendation approach based on granular association rules.First,we used granules to describe users and items.Then we generated rules between users and items through satisfying four measures of granular association rules.Finally,we matched the suitable rules to recommend items to users.Experiments were undertaken on a publicly available dataset MovieLens.Results show that granular association mining rule can be used for the recommendation on training and testing sets effectively and accurately." @default.
- W2394075573 created "2016-06-24" @default.
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- W2394075573 date "2014-01-01" @default.
- W2394075573 modified "2023-09-22" @default.
- W2394075573 title "Cold-start Recommendation Based on Granular Association Rules" @default.
- W2394075573 hasPublicationYear "2014" @default.
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