Multi-Classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce

Li, Qiang; Gu, Maojie; Zhou, Keren; Sun, Xiaoming

doi:10.1109/icdmw.2015.172

Cited by 17 publications

(26 citation statements)

References 3 publications

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“…In his research also mentioned the population of gender also affect the habit of shopping online [21]. In Indonesia, sellers easily offer their products on more than one M-Commerce app [22]. Table II shows that many sellers in Indonesia offer more products on more than one M-Commerce.…”

Section: A Characteristics Of Buyers and Sellers In Indonesiamentioning

confidence: 94%

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Analysis of Consumer Confidence on Mobile Commerce in Indonesia

Prabawati

Widyana

Suyoto

2018

EECSI

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The rapid development of mobile-based information technology, can change an existing business process. Such as loss of distributors in a single chain of business processes. Mobile Commerce industry is getting a good condition to grow. With the decrease in Mobile communication costs, more and more people are using mobile computing devices that can connect to the Internet. There are several M-Commerce applications in Indonesia, namely Lazada, Tokopedia, Buka Lapak, Shoppee and etc. The existence of these applications in Indonesia traders switches to online stores. This study takes three examples of M-Commerce applications in Indonesia with the top three criteria in terms of the best reviews and the highest number of reviews. The results of the sampling application are Lazada, Tokopedia and Buka Lapak. The process of collecting data taken will be analyzed to know about the level of consumer confidence in an M-Commerce application. This is evidenced by a collection of examples of online stores that exist in M-Commerce applications that inform ratings, discount information, and existing features. The results obtained from this research is, consumer confidence can be seen from the review feedback that exists in each-each M-Commerce. Many discounts and low prices have no effect on consumer buying interest in the online store. The researcher's suggestion is to reward the most traded buyers with points that can be redeemed for certain goods, discounted goods or subsidized postage.

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Section: A Characteristics Of Buyers and Sellers In Indonesiamentioning

confidence: 94%

“…Each M-Commerce must also have controls on the comments section of every item and store listed in M-Commerce. The comment section is one part that is first read and reviewed by every consumer who will buy a product [22]. Other research also needs to study the inhibiting effects of an M-Commerce business process.…”

Section: Contribution and Implicationmentioning

confidence: 99%

Analysis of Consumer Confidence on Mobile Commerce in Indonesia

Prabawati

Widyana

Suyoto

2018

EECSI

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“…In the ensemble learning model part, Bagging fusion strategy was used to construct RF and GDBTbased ensemble learning models, and the results showed that GDBT ensemble learning model had higher F1 score than RF ensemble learning model in the O2O prediction of morrow purchasing behaviors. Literature [9] also used ensemble learning model of GDBT-based learner, but different from fusion strategy in Literature [8], it used Blending fusion strategy with better effect than Bagging fusion strategy. Li et al adopted Stacking fusion strategy to construct an ensemble learning model of GDBT-based learner and achieved better effect than Bagging and Blending [7].…”

Section: Ensemble Learning Prediction Modelmentioning

confidence: 99%

“…Li et al adopted Stacking fusion strategy to construct an ensemble learning model of GDBT-based learner and achieved better effect than Bagging and Blending [7]. Literatures [7][8][9] demonstrated the feasibility of the improved fusion strategy to improve the performance of ensemble learning prediction model, thus providing a theoretical foundation for FCV-Stacking fusion strategy. Zhou et al put forward a two-layer Multi-Model Stacking Ensemble (MMSE) Learning, where the first layer trained four ensemble algorithms-RF, AdaBoost, GDBT and XGBoost-as base learners, the second layer used XGBoost algorithm to combine the four base learners and output the final prediction result, and the result indicated that its performance was more outstanding than single ensemble algorithm-based prediction model [27].…”

Section: Ensemble Learning Prediction Modelmentioning

confidence: 99%

“…In addition, CF algorithm excessively relies upon user ratings, so it cannot accurately predict user purchasing behaviors without user ratings or with user rating errors. Therefore, numerous researchers have proposed individual learning prediction models [6,7] such as logistic regression, support vector machine (SVM), multilayer perceptron (MLP) and neural network as well as ensemble learning prediction models [8][9][10][11][12] such as gradient boosting decision tree and XGBoost. In these models, the prediction of user purchasing behaviors is regarded as a binary classification problem in machine learning (positive example: purchase, negative example: not purchase).…”

Section: Introductionmentioning

confidence: 99%

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Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Zhang

Dong

2020

Preprint

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With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors were extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

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Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

Cirqueira

Hofer²,

Nedbal

et al. 2020

New Frontiers in Mining Complex Patterns

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Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online.

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Multi-Classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce

Cited by 17 publications

References 3 publications

Analysis of Consumer Confidence on Mobile Commerce in Indonesia

Analysis of Consumer Confidence on Mobile Commerce in Indonesia

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

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