Robust weighted SVD-type latent factor models for rating prediction

Gu, Yiqi; Yang, Xi; Peng, Mengjiao; Lin, Guang

doi:10.1016/j.eswa.2019.112885

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…To avoid over-fitting that can occur when learning the model parameters, regularization terms are often included to constrain the parameter values. In [28], the weights of users and items are learned and applied directly to the optimization function; these weights attempt to reduce the impact of malicious users who tend to give bad or incorrect ratings. Several variations of MF were proposed in [29] for regularizing the optimization of matrix-completion based on the frequency of users and items contribution.…”

Section: Related Workmentioning

confidence: 99%

Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender Systems

Patil¹,

Chapaneri²,

Jayaswal³

2022

IEEE Access

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As an essential task for recommender systems, the rating prediction problem over several contexts has attracted more attention over the recent years. The traditional approaches ignore the contexts and thus fail to predict the ratings for the unseen data in the rating tensor for varying contextual scenarios. Matrix factorization is preferred over decomposing the rating tensor for avoiding the burden of very high computational complexity while learning the interaction of users' and items' latent features. In this work, we propose a novel kernel loss function for optimizing the objective function of matrix factorization in a non-linearly projected rating space under multiple contexts in an optimum manner and also incorporate the implicit feedback of items in the learning process. Further, the optimization is regularized by applying different weights for each regularization term depending on the users' and items' participation. Extensive experimental evaluation on five benchmark context-aware datasets indicates the superiority of the proposed work for capturing the non-linearity and predicting the ratings of unseen items for users under varying contexts over the existing and baseline methods. The proposed kernel loss function is also shown to be resistant against shilling attacks in the recommender system. A detailed ablation study demonstrates the validity of the proposed work and the results are shown to be statistically significantly better with an improvement in the range of 3% to 11% over the baseline methods.

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Section: Related Workmentioning

confidence: 99%

Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender Systems

Patil¹,

Chapaneri²,

Jayaswal³

2022

IEEE Access

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“…Researchers have also found that various factors such as cost performance (Chen and Xu, 2016), customer's sentiment polarity (Geetha et al, 2017), social influence bias and ratings bubbles (Aral, 2014) affect the overall ratings of products or services. Researchers have also used different techniques like, tensor factorization method (Chambua et al, 2018), latent factor models (Gu et al, 2020), convolution neural networks (Khan and Niu, 2021), etc., to predict ratings from textual reviews. Since online reviews and ratings serve as great information sources (Engler et al, 2015) for users as well as providers (Chatterjee, 2019), determining ratings from SM posts can be really useful from the business perspective.…”

Section: Online Reviews and Online Ratingsmentioning

confidence: 99%

“…Researchers have also used different techniques like, tensor factorization method (Chambua et al. , 2018), latent factor models (Gu et al. , 2020), convolution neural networks (Khan and Niu, 2021), etc., to predict ratings from textual reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Ray

Bala

Rana

et al. 2022

AJIM

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PurposeThe widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.Design/methodology/approachIn this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.FindingsOverall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.Originality/valueThis study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

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“…It can deal with data sparseness and can give good recommendations. In [30], the author attach weights to latent factor models, the weights are computed by SVD model on the sparse matrix. CBE-CF algorithm [31] uses bi-clustering method to classify the rating matrix, furthermore,calculates the information entropy to update the cluster center to find the neighbor users, this method cope with the data sparsity.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Similarity Computing Model of Collaborative Filtering

2020

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Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality. INDEX TERMS Recommender system, collaborative filtering, context information.

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Robust weighted SVD-type latent factor models for rating prediction

Cited by 16 publications

References 11 publications

Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender Systems

Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender Systems

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

A New Similarity Computing Model of Collaborative Filtering

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