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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
Collaborative Filtering Recommendation Model Based on k-means Clustering

Authors: Nadia Fadhil AL-Bakri --- Soukaena Hassan Hashim
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2019 Volume: 22 Issue: 1 Pages: 74-79
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

In this age of information load, it becomes a herculean task for user to get the relevant things from vast number of information. This huge number of data demand specially designed Recommender system that can plays an important role in suggesting relevant information preferred by the users. From this point, this paper presents a modest approach to enhance prediction in MovieLens dataset with high scalability by applying user-based collaborative filtering methods on clustered data. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting K-means clustering and correlation coefficient similarity measures against MovieLens datasets lead to an increase in the scalability of recommender system.


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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