Improved Fuzzy C-Means Clustering for Personalized Product Recommendation

Wu, Juebo; Wu, Zongling

doi:10.19026/rjaset.6.4092

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…AdaBoost forms a linear combination 20 of selected classifier instances to create an overall ensemble. AdaBoost-based ensembles rarely over-fit a solution even if a large number of base classifiers in-stance are used ( Lei & He, 2017 ) and it minimizes an exponential loss function by fitting a stage-wise additive model ( Wu & Wu, 2013 ). As the minimization of classification error implies an optimization of a non-smooth, non-differentiable cost function which 25 can be best approximated by an exponential loss ( Koren, Bell & Volinsky, 2009 ), AdaBoost therefore per-forms extremely well over a wide range of classification problems.…”

Section: Methodsmentioning

confidence: 99%

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Zhang

et al. 2023

PeerJ Computer Science

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In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality.

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Section: Methodsmentioning

confidence: 99%

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Zhang

et al. 2023

PeerJ Computer Science

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“…The selected classifiers are combined to form a strong ensemble. AdaBoost-based ensembles rarely overfit a solution even if a large number of base classifier instances are used [13] and it minimizes an exponential loss function by fitting a stage-wise additive model [14]. As the minimization of classification error implies an optimization of a non-smooth, non-differentiable cost function that 25 can be best approximated by an exponential loss [15].…”

Section: Ensemble Learningmentioning

confidence: 99%

HABE: hybrid AdaBoost ensemble method of probabilistic matrix factorization recommendation system

Zhang¹,

Tu²,

Wu³

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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The PMF model is effective for addressing high-dimensional, large-scale, sparse, and imbalanced rating data, yet it may suffer from limitations in generalization and prediction accuracy in certain scenarios. To address these limitations, we propose a hybrid AdaBoost ensemble method within the PMF model. In this paper, we use two-stage algorithms in the model. Our approach uses a two-stage algorithm, whereby the first stage involves fuzzy clustering to calculate the scoring matrix of user-items, followed by neural network training to further enhance scoring prediction accuracy. The second stage involves using the rating matrix as the basis learner for training by different neural networks, and the final score prediction result is obtained through ensemble learning. Our proposed model was evaluated on the MovieLens and FilmTrust datasets, and its effectiveness was demonstrated. Due to its well-crafted architecture and robust representation learning capability, our model can be readily applied to various PMF model settings, such as PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models. The experiments show that the mean absolute error(MAE) of the proposed method increases by 1.24% and 0.79% compared with the Bagging-BP-PMF model on two different datasets, and the root mean square error(RMSE) increases by 2.55% and 1.87%, respectively. Finally, our experiments show that our proposed approach performs well in various settings. By utilizing ensemble learning to train the weight of the base learner from different neural networks, our method improves the stability of score prediction. Additionally, our results verify the universality of our approach.

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“…Following the extraction, classification of the browsing patterns and prediction of user future requests started. Wu and Wu (2013) adjusted the membership and density functions and improved the conventional C-means clustering algorithm in order to solve problem in which the number of clusters used to determine the convergence of the objective function was inadequate. Next, personal preferences were divided into several groups in a way that users with similar preferences were put into the same group.…”

Section: Related Workmentioning

confidence: 99%

“…The vector of user browsing patterns shows a condensed view of the behavior of a group of users based on their common interests and information needs (Wu et al, 2013). These movement patterns are used to determine the similarity between the new profiles and previous ones.…”

Section: Fig 2 Fcm Clustering Algorithm With S(c) Criteriamentioning

confidence: 99%

Constructing a web recommender system using web usage mining and user’s profiles

Mombeini¹,

Harounabadi²,

Sheshdeh³

2014

10.5267/j.msl

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The World Wide Web is a great source of information, which is nowadays being widely used due to the availability of useful information changing, dynamically. However, the large number of webpages often confuses many users and it is hard for them to find information on their interests. Therefore, it is necessary to provide a system capable of guiding users towards their desired choices and services. Recommender systems search among a large collection of user interests and recommend those, which are likely to be favored the most by the user. Web usage mining was designed to function on web server records, which are included in user search results. Therefore, recommender servers use the web usage mining technique to predict users' browsing patterns and recommend those patterns in the form of a suggestion list. In this article, a recommender system based on web usage mining phases (online and offline) was proposed. In the offline phase, the first step is to analyze user access records to identify user sessions. Next, user profiles are built using data from server records based on the frequency of access to pages, the time spent by the user on each page and the date of page view. Date is of importance since it is more possible for users to request new pages more than old ones and old pages are less probable to be viewed, as users mostly look for new information. Following the creation of user profiles, users are categorized in clusters using the Fuzzy C-means clustering algorithm and S(c) criterion based on their similarities. In the online phase, a neural network is offered to identify the suggested model while online suggestions are generated using the suggestion module for the active user. Search engines analyze suggestion lists based on rate of user interest in pages and page rank and finally suggest appropriate pages to the active user. Experiments show that the proposed method of predicting user recent requested pages has more accuracy and cover than other methods.Growing Science Ltd. All rights reserved. 4

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Improved Fuzzy C-Means Clustering for Personalized Product Recommendation

Cited by 4 publications

References 13 publications

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

HABE: hybrid AdaBoost ensemble method of probabilistic matrix factorization recommendation system

Constructing a web recommender system using web usage mining and user’s profiles

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