Addressing Data Sparsity in Collaborative Filtering Based Recommender Systems Using Clustering and Artificial Neural Network

Althbiti, Ashrf; Alshamrani, Rayan; Alghamdi, Tami; Ma, Xiaogang

doi:10.1109/ccwc51732.2021.9376008

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…We divided the test set using 10-fold cross-validation [76,78,79] for items in the input matrix. For each test set, we derived the mean absolute error (MAE) and root mean square error (RMSE) [78]; Equations ( 5) and (6) show the calculations for the MAE and RMSE, respectively:…”

Section: Experimental Designmentioning

confidence: 99%

“…Accordingly, there is a vast amount of information not only in digital content-related services but also in various services that provide product sales, maps, and search results. Various studies on providing such large amounts of information more effectively to users by utilizing not only basic machine learning techniques but also deep neural network approaches have been conducted [1][2][3][4][5][6][7][8]. Services that require more personalized information, such as e-commerce Mathematics 2023, 11, 2962 2 of 25 or digital content services, have raised the need for research on personalized recommender systems [1,5,[8][9][10][11][12] to provide more accurate information catering to each individual user.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

Eui

Choi

Lee³

et al. 2023

Mathematics

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Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems.

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Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

Eui

Choi

Lee³

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Recently, various studies related to the improvement of the data sparsity have been conducted [51,52]. These studies are not only attempting to improve based on the existing CF method, but also are being studied based on various methods applying deep learning approaches [52][53][54][55].…”

Section: Studies For the Recommendation Systems To Improve The Sparsi...mentioning

confidence: 99%

“…Deep learning-based studies are being conducted using an integrated method of existing CF and neural networks or learning cross-domain. Althbiti et al [52] propose a novel model based on artificial neural network model CANNBCF (Clustering and Artificial Neural Network-Based Collaborative Filtering) to improve the data sparsity in collaborative filtering. They utilize various domains including books, music, jokes, and movies to evaluate the proposed model.…”

Section: Studies For the Recommendation Systems To Improve The Sparsi...mentioning

confidence: 99%

“…In order to alleviate the cold-start and sparsity problem many studies currently construct and propose a method of predicting users' preferences for items using information, such as the feature of items [28][29][30][31][32]. In addition, various studies have been conducted to predict users' evaluation by extracting and analyzing feature vectors of items using deep learning approaches [52][53][54][55]. However, current studies are attempting research that transforms the shape of the existing system using metadata of users or items.…”

Section: Analysis and Motivationmentioning

confidence: 99%

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Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features

Choi

Lee²,

Jang

et al. 2023

Mathematics

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With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve the dataset sparsity and increase the accuracy of the prediction results by using item features with user responses. A method based on the content-based filtering concept is proposed to extract category rates from the user–item matrix according to the user preferences and to organize these into vectors. Thereafter, we present a method to filter the user–item matrix using the extracted vectors and to regenerate the input matrix for collaborative filtering (CF). We compare the prediction results of our approach and conventional CF using the mean absolute error and root mean square error. Moreover, we calculate the sparsity of the regenerated matrix and the existing input matrix, and demonstrate that the regenerated matrix is more dense than the existing one. By computing the Jaccard similarity between the item sets in the regenerated and existing matrices, we verify the matrix distinctions. The results of the proposed methods confirm that if the regenerated matrix is used as the CF input, a denser matrix with higher predictive accuracy can be constructed than when using conventional methods. The validity of the proposed method was verified by analyzing the effect of the input matrix composed of high average ratings on the CF prediction performance. The low sparsity and high prediction accuracy of the proposed method are verified by comparisons with the results by conventional methods. Improvements of approximately 16% based on K-nearest neighbor and 15% based on singular value decomposition, and a three times improvement in the sparsity based on regenerated and original matrices are obtained. We propose a matrix reconstruction method that can improve the performance of recommendations.

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A Hybrid Collaborative Filtering Based Recommender Model Using Modified Funk SVD Algorithm

Klymash,

Hordiichuk-Bublivska,

Pyrih

et al. 2024

Lecture Notes in Electrical Engineering

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Addressing Data Sparsity in Collaborative Filtering Based Recommender Systems Using Clustering and Artificial Neural Network

Cited by 13 publications

References 25 publications

A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features

A Hybrid Collaborative Filtering Based Recommender Model Using Modified Funk SVD Algorithm

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