Cooperative Filtering Program Recommendation Algorithm Based on User Situations and Missing Values Estimation

Dong, Jian; Tang, Ruichun; Lian, Geqiang

doi:10.1007/978-3-030-00828-4_25

Cited by 1 publication

(2 citation statements)

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“…The main advantage of EMDP is that it can avoid poor imputation (no or insufficient amount of imputed data) because it imputes all the data whose similarities with the active user and active item exceed the thresholds. However, the main disadvantage of EMDP is a relatively poor accuracy because it gives the same value to all missing data [17].…”

Section: Related Studiesmentioning

confidence: 99%

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A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

et al. 2021

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Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

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Section: Related Studiesmentioning

confidence: 99%

“…According to the prerequisite for neighborhood-based CF defined by [17], the similar nearest neighbor has the most information regarding the data to be predicted. Accordingly, k-RRI is implemented according to the criteria defined below.…”

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confidence: 99%