Two Models Based on Social Relations and SVD++ Method for Recommendation System

Sabaawi, Ali M. Ahmed Al; Karacan, Hacer; Yenice, Yusuf E.

doi:10.3991/ijim.v15i01.17751

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…The SVD is good for with few datasets and it can improve performance on many algorithms. It majorly uses the Principal component analysis(PCA) [20] which is useful for dimensional reduction. [20] The Single Value Decomposition (SVD++) is extension of SVD algorithm, with considering implicit ratings This is equivalent to Probabilistic Matrix Factorization algorithm.It Constructs a matrix with the row of users and columns of items and the elements are given by the users' ratings.…”

Section: A = Usv ⊤mentioning

confidence: 99%

MF-RISE : Benchmarking for Multifaceted Recommender System Engine

Mali

Mishra

Vijayalakshmi

2023

Preprint

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The advent of the information age has led tothe availability of overwhelming choices of products to userswhich create need of various Recommendation Systems (RS). Recommendation System belongs to the methods of InformationRetrieval, Data Mining and Machine Learning algorithms. Multifaceted Recommendation System Engine (MF-RISE) helpthe users to get personalized recommendations, helps users to select correct product from wide range of products using userfeedback, ratings and reviews provided by users. In real-world scenarios, recommenders have many non-functionalrequirements of technical nature and must handle huge amountdata. Evaluation of Multifaceted Recommendation System Enginemust take these issues into account in order to be producemaximum useful recommendations. The many researchers have proposed a wide range of recommendationsystems algorithms. This study investigates there are threepopular existing types of recommendation systems algorithms, Collaborative Filtering (CF), Content-Based Filtering (CB), andHybrid Recommendation System. The MovieLens dataset with its3 variants was utilized for the purpose of this study. The studiedevaluation methods consider both quantitative and qualitativeaspects of algorithm with many evaluation parameters like meansquared error (MSE), root mean squared error (RMSE), Test Timeand Fit Time are calculated for each recommender algorithmimplementation. The study identifies the gaps and challengesfaced by every recommender algorithm. This study will alsohelp researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

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Section: A = Usv ⊤mentioning

confidence: 99%

MF-RISE : Benchmarking for Multifaceted Recommender System Engine

Mali

Mishra

Vijayalakshmi

2023

Preprint

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“…Ref. [48] considers for example a 2D user-item matrix, where p represents rows of users, and q denotes the columns of items. The user rating value for items is r u,i .…”

Section: • Svd++mentioning

confidence: 99%

“…• |R i | represents the "number of items" that have been evaluated by multiple users; • y j designates the left orthogonal of implicit matrix. • λ1, λ2 are additional parameters added to values |R u | and |R i | for regularization [48].…”

mentioning

confidence: 99%

Personalized E-Learning Recommender System Based on Autoencoders

El Youbi El Idrissi,

Akharraz,

Ahaitouf

2023

ASI

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Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).

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“…It majorly uses the Principal component analysis(PCA) which is useful for dimensional reduction. [20] The Single Value Decomposition (SVD++) is extension of SVD algorithm, with considering implicit ratings This is equivalent to Probabilistic Matrix Factorization algorithm. It Constructs a matrix with the row of users and columns of items and the elements are given by the users' ratings.…”

Section: B Matrix Factorization Algorithmmentioning

confidence: 99%

Benchmarking for Recommender System (MFRISE)

Mali¹,

Mishra²,

Vijayalaxmi³

2022

3C TIC

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The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

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Two Models Based on Social Relations and SVD++ Method for Recommendation System

Cited by 7 publications

References 27 publications

MF-RISE : Benchmarking for Multifaceted Recommender System Engine

MF-RISE : Benchmarking for Multifaceted Recommender System Engine

Personalized E-Learning Recommender System Based on Autoencoders

Benchmarking for Recommender System (MFRISE)

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