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Article
Reducing Data Sparsity in Recommender Systems

Authors: Nadia F. Al-Bakri --- Soukaena Hassan Hashim
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2018 Volume: 21 Issue: 2 Pages: 138-147
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

Recommender systems are used to find user's interested things among a huge amount of digital information. Collaborative filtering is used to generate recommendations. However, the data sparsity problem leads to generate unreasonable recommendations for those users who provide no ratings. From this point, this paper presents a modest approach to enhance prediction in movielens dataset with high sparsity by applying collaborative filtering methods. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting similarity measures against movielens user rating datasets show that the result of prediction is enhanced about 10% to15% with the non-sparse rating matrix.


Article
A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering

Authors: Nadia F. AL-Bakri --- Soukaena H. Hashim
Journal: Iraqi Journal of Science المجلة العراقية للعلوم ISSN: 00672904/23121637 Year: 2018 Volume: 59 Issue: 2B Pages: 934-945
Publisher: Baghdad University جامعة بغداد

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Abstract

Production sites suffer from idle in marketing of their products because of the lack in the efficient systems that analyze and track the evaluation of customers to products; therefore some products remain untargeted despite their good quality. This research aims to build a modest model intended to take two aspects into considerations. The first aspect is diagnosing dependable users on the site depending on the number of products evaluated and the user's positive impact on rating. The second aspect is diagnosing products with low weights (unknown) to be generated and recommended to users depending on logarithm equation and the number of co-rated users. Collaborative filtering is one of the most knowledge discovery techniques used positively in recommendation system. Similarity measures are the core operations in collaborative filtering; however, there is a certain deviance through using traditional similarity measures, which decreases the recommendation accuracy. Thus, the proposed model consists of a combination of measures: constraint Pearson correlation, jaccard distance measure and inverse user frequency (IUF). The experimental results implemented on movielens data set using MATLAB show a comparison between the results of the proposed model and some of the traditional similarity measures. The outcome results of the comparison show that the proposed model can be used as a parameter in the prediction process to achieve accurate prediction results during recommendation process

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