Fuzzy Tools in Recommender Systems: A Survey

Toledo, Raciel Yera; Martínez, Luis

doi:10.2991/ijcis.2017.10.1.52

Cited by 152 publications

(88 citation statements)

References 143 publications

(173 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another, area for future research might be to explore the use of GA to train artificial neural networks or a hybrid of GA with other popular training algorithms like the gradient-descent backpropagation algorithms to train the network. Moreover, a greater focus on combining fuzzy-based approaches 45 with GA, to estimate the overall rating could provide impressive results.…”

Section: Discussionmentioning

confidence: 99%

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

Hassan¹,

Hamada²

2018

IJCIS

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Hassan¹,

Hamada²

2018

IJCIS

View full text Add to dashboard Cite

show abstract

“…Finally, 59464 links remain in the network. The second one is the Movielens data which is a random sample of the whole records of users ratings during the seven-month period from 19 September 1997 to 22 April 1998 in Movielens.com (http :/ /www.grouplens .org/). It consists of 943 users, 1682 movies, and 100000 links.…”

Section: Data and The Correlation Of Objects' Degreementioning

confidence: 99%

“…This survey can greatly help the researchers to promote their understandings about the application development of recommender system. In addition, Yera et al [22] presented a review about the application of fuzzy tools in this research. According to his research, application of fuzzy tools is mainly used to detect more exclusively studied topic and also research gaps and hence to make proper suggestions for the subsequent researches to promote the further development of the fuzzy-based recommender system.…”

Section: Introductionmentioning

confidence: 99%

Information filtering in evolving online networks

Chen

Zhang

et al. 2018

Physics Letters A

View full text Add to dashboard Cite

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.

show abstract

“…Different filtering techniques such as collaborative filtering, demographic filtering, content-based filtering, and hybrid filtering have been widely used to characterize the functions of RSs [11]. Furthermore, recent surveys like the one conducted by Yera and Martinez [12] have enlightened us on the use of fuzzy techniques for supporting RSs. Moreover, as these traditional techniques have some shortcomings, several approaches such as incorporating trust statements into online RSs have been proposed to improve the accuracy of the systems [1,13].…”

Section: Recommender Systems (Rss)mentioning

confidence: 99%

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

View full text Add to dashboard Cite

Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques.

show abstract

Fuzzy Tools in Recommender Systems: A Survey

Cited by 152 publications

References 143 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

Information filtering in evolving online networks

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Contact Info

Product

Resources

About