Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System

Kuang, Guofang; Li, Yuanchen

doi:10.11591/telkomnika.v12i2.3983

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…(FIS An FRB which is a core component of FIS initially comes from experts or operators. The presence of many empirical data in the real world has led some researchers for developing methods in generating FRB which are the lookup table schemes by Wang and Mendel [12], heuristic method based on numerical data by Nozaki et al [13], and the association rule mining method by both of Kuang, and Li [14] and Arif, et al [15]. The methods of generating the FRB above produce an FRB with a large number of rules that result in a slow inference process which affects that the implementation of FIS will consume much time.…”

Section: Figure 1 the Components Of Fuzzy Inference Systemmentioning

confidence: 99%

Generating of Fuzzy Rule Bases with Gaussian Parameters Optimized via Fuzzy C-Mean and Ordinary Least Square

2019

IJRTE

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The fuzzy rule bases has a central role in the fuzzy inference system. Generating of rule bases based on input-output data pairs by using Fuzzy C-Mean clustering (FCM) requires parameters setting of the membership function (mf). In the Gaussian mf, the mean and spread parameters must be determined. The outputs of FCM clustering include the cluster center and the partition matrix of each object. The value of cluster center can be used as the center parameter, but the spread parameter is usually determined as a constant value. The research proposes a method to determine spread parameter of Gaussian mf by using Ordinary Least Square (OLS) approach with using the partition matrix as the membership degree of each object in the cluster. The magnitude of cluster centers (n) of 3, 5, and 7 are considered as FCM input to cluster the weekly price of soybeans in East Java, Indonesia on the period of January 2014 to December 2017. Based on each cluster center, the optimal spread value of a Gaussian mf is obtained via OLS. This research succeeded in getting a spread values that could approach almost perfectly each element of the partition matrix

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Section: Figure 1 the Components Of Fuzzy Inference Systemmentioning

confidence: 99%

Generating of Fuzzy Rule Bases with Gaussian Parameters Optimized via Fuzzy C-Mean and Ordinary Least Square

2019

IJRTE

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“…Compared with traditional transaction methods, the biggest advantage of e-commerce is that all the navigation data of all the visits made by customers on the e-commerce site are stored in the servers. From this behavioral and transactional information at the level of the individual customer (page viewed, sequence of visits, purchase process, number of transactions, etc ...) and in addition to RFM variables, many indicators can be extracted [55], and used as predictor variables by our models to improve their distinction power between customers totally churn and those who partially defect and those who remain loyal.An overview of all extracted variables used in this study is presented in Table 5. Table 6 summaries all behavioural independent variables supported by previous research in the both offline and online environments.…”

Section: Variables Operationalization 331 Predictors (Independent mentioning

confidence: 99%

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context

Rachid¹,

Amine²,

Bouikhalene³

et al. 2018

IJECE

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<p><span>With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers’ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.</span></p>

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“…Association rules are playing an important role in data mining-based applications. One of the well-known classic applications is market basket analysis [10]. It can analyze a set of transactions (also called itemsets) purchased by customers, and discover meaningful patterns (e.g., association among items).…”

Section: Related Researchmentioning

confidence: 99%

An Association Rule Algorithm for Online e-Commerce Recommendation Service

Dai¹,

Zeng²

2016

JOEBM

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Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System

Cited by 8 publications

References 12 publications

Generating of Fuzzy Rule Bases with Gaussian Parameters Optimized via Fuzzy C-Mean and Ordinary Least Square

Generating of Fuzzy Rule Bases with Gaussian Parameters Optimized via Fuzzy C-Mean and Ordinary Least Square

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context

An Association Rule Algorithm for Online e-Commerce Recommendation Service

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