A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy

Zheng, Bichen; Liu, Bingwei

doi:10.1109/icce.2018.8326351

Cited by 10 publications

(10 citation statements)

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“…Nowadays, users' spend more time on exploring different products and comparing products in different ecommerce platforms to find the most advantageous one in terms of price and quality [8]. Many well-known e-commerce platforms record users' activities and use this data to have personalised content by giving recommendations [13], [14], and purchase prediction in the sessions [43], [44].…”

Section: Related Work a Session Logsmentioning

confidence: 99%

Using Word2Vec Recommendation for Improved Purchase Prediction

Esmeli

Bader-El-Den

Abdullahi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.

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Section: Related Work a Session Logsmentioning

confidence: 99%

Using Word2Vec Recommendation for Improved Purchase Prediction

Esmeli

Bader-El-Den

Abdullahi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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“…x x CDM Handle of data unbalancing [28] x Feature engineering with recency and frequency of page views [29] x x CF Combination of features from clickstream and transactions for collaborative filtering [30] x Feature engineering for clickstream [31] x Combination of features from clickstream and transactions for collaborative filtering [32] x Feature engineering with product heterogeneity for collaborative filtering [33] x x x DLC Real-time predictions with ensemble and deep learning [34] x Recommendation of bundles of products considering quality and diversity criteria [35] Purchase Intent (PCI)…”

Section: Product (Ppd)mentioning

confidence: 99%

“…The results will be presented, reflecting those questions in Subsections 3.1 and 3.2. Examples of intention types reported in the literature are purchase oriented or general [35], browsing, searching, purchasing, and bouncing [37]. This task is essential for identifying similar groups of customers, and for applications in which customer segmentation is needed.…”

Section: A Conceptual Framework Of Analysis For Customer Purchase Prediction In Ecommercementioning

confidence: 99%

“…Those three identified tasks enable a variety of business intelligence applications for online retailers, such as: A) Product Recommendations [29]; B) Targeted Marketing [16,42]; C) Layout Personalization of E-commerce Landing Pages [17]; D) Load balance Optimization to Prioritize Quality of Service for Likely Buyers [14]; E) Stock Management Optimization of Products [28,32]; F) Real-time Customer Service [49]; G) Purchase Trends Discovery [15]; H) Offers Awareness Based on the Detected Intention of Consumers [35].…”

Section: A Conceptual Framework Of Analysis For Customer Purchase Prediction In Ecommercementioning

confidence: 99%

“…However, more investigation in the field of Feature Learning is recommended in this area, or the combination of those two methodologies in purchase prediction online. ─ Creation Process of Personalized Feature Engineering Functions: Some researchers explore the creation of personalized functions in Feature Engineering, such as the popularity of a product [17], the diversity of customer behavior [18,35] and graph metrics [21]. It could be relevant to map this creation process, and help other researchers in establishing such novel features for customer behavior online.…”

Section: Research Agendamentioning

confidence: 99%

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

Cirqueira

Hofer²,

Nedbal

et al. 2020

Lecture Notes in Computer Science

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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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