A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system

Jeon, Tae-Ryong; Cho, Jaewoo; Lee, Soojin; Baek, Gyeongdong; Kim, Sungshin

doi:10.1109/fuzzy.2009.5277415

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Clusters contribute to provide the recommendation, so that new items are suggested to the target user by evaluating the average rating of unrated items. Other applications of fuzzy logic to collaborative filtering have recently been proposed by different research works which introduced hybrid recommendation techniques based on fuzzy logic for suggesting to users items as movies (Jeon, Cho, Lee, Baek, & Kim, 2009) or books (Maatallah & Seridi, 2010) which users could consider interesting.…”

Section: Fuzzy Logic In Collaborative Filtering Techniquesmentioning

confidence: 99%

Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Birtolo

Ronca

2013

Expert Systems with Applications

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Section: Fuzzy Logic In Collaborative Filtering Techniquesmentioning

confidence: 99%

Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Birtolo

Ronca

2013

Expert Systems with Applications

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“…Siddiquee et al [13], Verma et al [14] and Jeon et al [15] focus on applying fuzzy logic in their studies.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…After the experiments, they observe that fuzzy clustering algorithm gets better results over k-means method. Jeon et al [15] use CF and fuzzy system for their prediction system and they observe that fuzzy system improves performance of CF system. Yigit Sert et al [16] aim to improve prediction accuracy of recommender systems using artificial bee colony and genetic algorithms.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving performance of them. In this study, prediction performances of neural networks were evaluated and their performances were improved using genetic algorithms. Performance of this study was compared with other studies, after that, superiority of this study was shown. While multilayer perceptron, generalized feed-forward network and CANFIS (Coactive Neuro Fuzzy Inference Systems) were used as a neural network algorithms, Movielens 100K and Movielens 1M datasets which are preferred in recommender systems studies mostly were used to train and test the system. Mean square error and root mean square error were employed as performance metrics. As a result, it was observed that genetic algorithm improves performance of neural networks and prediction performance of hybrid combination of neural networks and genetic algorithm is superior to prediction performance of recommender systems available in the literature.

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“…Beyond these two key researches, other authors such as Chao et al [27], Sobecki et al [116], Jeon et al [55], Nguyen and Duong [90] and Tiwari and Kaushik [124], have also developed more application-oriented recommendation approaches supported by fuzzy inference processes.…”

Section: Maementioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system

Cited by 12 publications

References 8 publications

Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

Evolutionary neural networks for improving the prediction performance of recommender systems

Fuzzy Tools in Recommender Systems: A Survey

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