Collaborative filtering recommender system is utilized as a significant method to suggest products to the users depends on their preferences. It is quite complicate when the user preference and rating data is sparse. Missing value occurs when there are no stored values for the specified dataset. Typical missing data are in three categories such as (i) Missing completely at random, (ii) Missing at random and (iii) Missing not at random. The missing values in dataset affect accuracy and causes deprived prediction outcome. In order to alleviate this issue, data imputation method is exploited. Imputation is the process of reinstating the missing value with substitute to preserve the data in dataset. It involves multiple approaches to evaluate the missing value. In this paper, we reviewed the progression of various imputation techniques and its limitations. Further, we endeavored k-recursive reliability-based imputation (k-RRI) to resolve the boundaries faced in existing approaches. Experimental results evince the studied methodology appreciably improves the prediction accuracy of recommendation system.

Sparse Data, Missing Value, Recommendation system, Missing Value Imputation, Recursive Imputation, Prediction

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