Pair-wise Preference Relation based Probabilistic Matrix Factorization for Collaborative Filtering in Recommender System

Pujahari, Abinash; Sisodia, Dilip Singh

doi:10.1016/j.knosys.2020.105798

Cited by 56 publications

(16 citation statements)

References 26 publications

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“…This method is the standard item-based CF that is based on neighborhood models in recommender systems [10,14,68]. We followed the setting of the existing literature to adapt it to an explicit dataset [2,72]. The most common item-based CF is a similarity measure between items, where sim(i, j) denotes the similarity of item i and item j.…”

Section: Build Several Types Of Recommender Systemmentioning

confidence: 99%

Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

Kim

Choi

2021

Sustainability

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Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.

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Section: Build Several Types Of Recommender Systemmentioning

confidence: 99%

Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

Kim

Choi

2021

Sustainability

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“…The latent features are defined using the singular value decomposition algorithm [22]. Many factorization methods integrate latent and baseline features of users and items utilizing several formulae [7]. For example, the baseline formula can be used to predict the missing rating scores in the rating matrix as shown in Equation (1).…”

Section: A Matrix Factorizationmentioning

confidence: 99%

“…For example, CF utilizes the rating scores of neighbours to predict a list of items to the active user. However, CF suffers from three main issues: data sparsity [3], [4], cold start [5], [6], and scalability [7] [8]. This paper briefly discusses the approaches utilized to solve the sparsity issue.…”

Section: Introductionmentioning

confidence: 99%

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Al-Hadi¹,

Alomari²,

Alshari³

et al. 2020

IJACSA

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“…The most popular models are Matrix Factorization (MF) and Neural Networks (NN). MF [14]- [18] models transform the rating matrix in two new matrices that contain a low-dimensional representation of users and items and can be used to recreate the original matrix as closely as possible. NNs [19] models use deep networks to extract and join latent information from users and items in a non-linear fashion, modeling users' preferences.…”

Section: Introductionmentioning

confidence: 99%

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset

González¹,

Ortega

Perez-Lopez³

et al. 2022

IEEE Access

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Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper seeks the origin of these biases and unfairness. To this end, it analyzes the demographic characteristics of a gold standard dataset and its prediction performance when used in a multitude of Recommender Systems. In addition, this paper proposes Soft Matrix Factorization (SoftMF), which tries to balance the predictions of different types of users to reduce the present inequality. The experimental results show that those biases and unfairness are not introduced by the different recommendation models and that they come from the socio-psychological and demographic characteristics of the used dataset. INDEX TERMSRecommender systems, collaborative filtering, fairness, MovieLens. SANTIAGO ALONSO received the B.S. degree in software engineering and the Ph.D. degree in computer science and artificial intelligence from the Universidad Politécnica de Madrid, in 2015. He is currently an Associate Professor with the Universidad Politécnica de Madrid, participating in master and degree subjects and doing work related with advanced databases. His main research interests include natural computing (P-systems) and did some work on genetic algorithms. His current research interests include machine learning, data analysis, and artificial intelligence.

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Pair-wise Preference Relation based Probabilistic Matrix Factorization for Collaborative Filtering in Recommender System

Cited by 56 publications

References 26 publications

Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset

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