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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


Article
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures
دراسة حول دقة التنبؤ في نظام التوصية على أساس مقاييس التشابه

Authors: Nadia Fadhil AL-Bakri ناديه فاضل البكري --- Soukaena Hassan Hashim سكينه حسن هاشم
Journal: Baghdad Science Journal مجلة بغداد للعلوم ISSN: 20788665 24117986 Year: 2019 Volume: 16 Issue: 1 Supplement Pages: 263-269
Publisher: Baghdad University جامعة بغداد

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Abstract

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

نظم التوصية هي أدوات لفهم الكم الهائل من البيانات المتاحة في عالم الإنترنت. التصفية التعاونية هي واحدة من أكثر تقنيات اكتشاف المعرفة المستخدمة بشكل إيجابي في نظام التوصيات. تركز التصفية التعاونية القائمة على الذاكرة على استخدام الحقائق حول المستخدمين القائمين والمتوفرين, للتنبؤ بأشياء جديدة للمستخدم المستهدف. مقاييس التشابه هي من العمليات الأساسية في التصفية التعاونية ودقة التنبؤ تعتمد في الغالب على حسابات التشابه. في هذه الدراسة ، تم استخدام مجموعة من مقاييس التشابه التقليدية مع المعاملات المرجحه لحساب العلاقة بين المستخدمين عبر مصفوفة التخمين لمجموعة بيانات MovieLens)). تم اكتشاف مزايا وعيوب كل مقياس. من الدراسة ، تم اقتراح مقياس جديد مكون من مجموعة من المقاييس للتعامل مع المعنى الشامل لتخمين مجموعة البيانات. بعد إجراء النتائج التجريبية ، تبين أن المقياس المقترح حقق العديد من الأهداف التي تزيد من دقة التنبؤات.

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