A Robust Multi-criteria Recommendation Approach with Preference-Based Similarity and Support Vector Machine

Fan, Jun; Xu, Linli

doi:10.1007/978-3-642-39068-5_47

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…For instance, in terms of improvements with regard to prediction accuracy, the MGA in Table 3 column one has MAE = 0.961 and RMSE = 1.396, and the corresponding single rating SVD in Table 1 × 100 52.68% and similarly, the percent decrease in RMSE = 51.14%. This is far better than the 16% decrease in MAE of support vector machine approach that was investigated by Fan and Xu 14 , not to talk of a linear regression method, which only reduces the error by 15% 35 . The same comparison can be made with other approaches such as the fuzzy-based approach 38 , the hybrid approach 37 which combined selforganizing map (SOM) with fuzzy techniques to improve the prediction accuracy, Jannach et al 27 who proposed a support vector regression method for improving the accuracy of multi-criteria, and so on.…”

Section: Comparative Analysismentioning

confidence: 79%

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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We often make decisions on the things we like, dislike, or even don't care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user's preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms' methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

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Section: Comparative Analysismentioning

confidence: 79%

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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“…As an example of such cases, the AsymSVD predicted 5.7 instead of 13, and 10.3 instead of 2. However, while the result is [12] 44.44% 32.60% Lakiotaki et al [28] 50.79% 48.13% Jannach et al [32] − 29.62% Fan et al [33] 16.00% − Sahoo et al [34] 49.64% − not generally bad, these discrepancies are also attributed to the problems of prediction accuracy of the AsymSVD. On the other hand, the correlation between the predictions of the proposed ANN-based model and the actual ratings presented in Fig.…”

Section: Resultsmentioning

confidence: 95%

A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

Hassan¹,

Hamada²,

Yusuf³

2020

IJACSA

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Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recommendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric singular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multicriteria recommendation technique.

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“…Even though the work presented in [36] utilizes denoising autoencoders to extract features from content information, the work is still based on linear assumption during prediction process. [37] propose to use preference-based similarity instead of computing correlations over sparse rating profiles to handle with sparsity issue. Additionally, reducing dimensions of users'/items' sparse preferences into low-dimensional dense space helps to deal with both sparsity and scalability issues.…”

Section: Related Workmentioning

confidence: 99%

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide their users an efficient way to handle with information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix which constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences over under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multi-criteria recommender systems are an extension of traditional recommender systems that utilize multi-criteria-based user preferences. Multi-criteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multi-criteria recommender systems may fail to find similar neighbors to an active user due to the lack of co-rated items among users.Therefore, we propose a new similarity-based multi-criteria collaborative filtering approach based on autoencoders. In order to handle with sparsity, the proposed method extracts non-linear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multi-criteria recommendation techniques.

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A Robust Multi-criteria Recommendation Approach with Preference-Based Similarity and Support Vector Machine

Cited by 11 publications

References 12 publications

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

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