Analyzing customer behavior from shopping path data using operation edit distance

Syaekhoni, M. Alex; Lee, Chanseung; Kwon, Young S.

doi:10.1007/s10489-016-0839-2

Cited by 23 publications

(9 citation statements)

References 29 publications

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“…An MDS algorithm [21], which can enhance recognition efficiency and reduce the computational burden of the QCM-based e-nose, was used for dimensionality reduction. MDS algorithms take an input matrix of dissimilarities between pairs of items and output a coordinate matrix.…”

Section: Experiments and Methodsmentioning

confidence: 99%

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Jia

2017

Sensors

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Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

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Section: Experiments and Methodsmentioning

confidence: 99%

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Jia

2017

Sensors

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“…In order to quantify the time change, this paper adopts the method of time series similarity analysis. The commonly time series similarity analysis methods include dynamic time warping (DTW), 6 edit distance on real sequences (EDR) 7 etc. In this paper, we adopt TWED 8 to analyze the temporal information in the urban functional semantic themes.…”

Section: Spatio‐temporal Topic Model and Urban Functional Semantic Measurementmentioning

confidence: 99%

Spatio‐temporal topic model induced semantic function measurement for urban landscape design in smart city

Wang

2020

Internet Technology Letters

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The traditional semantic measurement methods cannot take into account specific urban functions and cannot extract spatial and temporal semantics in urban landscape design. In order to solve this issue, this paper proposes a spatio‐temporal semantic measurement method that takes into account urban functions. By improving the traditional latent Dirichlet allocation model, the designed spatio‐temporal topic model takes into account the semantics of urban functions via constructing spatial and temporal documents. Then, a similarity measurement is proposed for urban functional analysis based on spatio‐temporal topic model. Lastly, a hierarchical clustering is adopted to realize the measurement of urban functional semantics based on the spatio‐temporal topic model.

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“…Techniques such as an eye tracking tool from Chandon et al (2009), wireless local area networks and radio frequency identification (RFID) (Uotila and Skogster, 2007; Syaekhoni et al , 2017) have also shown to be helpful at elucidating in-store customer buying behavior. In addition, customer behavior analyses have been proposed for many different real-world applications, including cross-marketing in business domains (Agrawal and Srikant, 1994; Liu et al , 2009; Han et al , 2000; Adnan and Alhajj, 2009), customer pattern segmentation (Syaekhoni et al , 2018) and mobile environment planning (Dubey and Shandilya, 2010; Kushwaha and Sharma, 2010; Kim and Park, 2011; Lee et al , 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Customer behavior analysis using real-time data processing

Alfian

Ijaz

Syafrudin

et al. 2019

APJML

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Purpose The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data. Design/methodology/approach In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality. Findings First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations. Research limitations/implications This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario. Practical implications The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer. Social implications The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability. Originality/value The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

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Analyzing customer behavior from shopping path data using operation edit distance

Cited by 23 publications

References 29 publications

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Spatio‐temporal topic model induced semantic function measurement for urban landscape design in smart city

Customer behavior analysis using real-time data processing

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