Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior

Guo, Long; Hua, Lifeng; Jia, Rongfei; Zhao, Binqiang; Wang, Xiaobo; Cui, Bin

doi:10.1145/3292500.3330670

Cited by 91 publications

(69 citation statements)

References 21 publications

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“…Prediction goals. Some models address the challenge of distinguishing between buying and non-buying sessions (B/NB, two possible prediction outcomes) [4], [7], [8], [29]- [35]. Alternatively, other works concentrate on calculating the probability that a customer buys either a specific product (B-Prod) [20], [36]- [38] or a class of products (B-CProd) [39], [40], makes a purchase in the next visit to the online store (Next) [41]- [43], or repurchases in a future session (ReP) [44], [45].…”

Section: Related Workmentioning

confidence: 99%

“…As shown in Table 1, the most common attributes are customers' personal (P) or demographic (D) data, product interest scores (PI), customers' navigation (NB) or purchasing behaviors (PB), or historical purchasing data (HP) (the RFM value or payments, for instance). Nevertheless, some proposals select alternative interesting attributes, such as the use of shopping carts (SC) [32], [43], seller's reputations and facilities (SRF) [45], customers' opinions (CO) [47], changes in user behavior (ChB) [46] or interactions of users with Web pages and their elements (Int) [7]. From a methodological point of view, two approaches should be emphasized.…”

Section: Related Workmentioning

confidence: 99%

“…Among the classification methods, the most common are Neuronal Networks (NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forest (RF) and Naives-Bayes models (NBM). The approaches based on these methods make their predictions using a single classifier (S-Class, [33], [34], [43], [46]) or by combining multiple classifiers to improve the accuracy of the results (M-Class, [7], [8], [29]- [31], [40], [42], [44], [45]). In the last case, a combination algorithm integrating the predictions of the different classifiers is needed.…”

Section: Related Workmentioning

confidence: 99%

“…The renewed interest in artificial intelligence techniques has led to a proliferation of methods for predicting the future behavior of e-commerce customers [7], [8]. Most of these methods address the prediction problem from the perspective of aggregated data, providing high-level predictions as a result.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites

2020

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Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this paper, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile. INDEX TERMS Behavior prediction, User profiling, Log analysis, Clustering, Neural networks, Model checking.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites

2020

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“…However, the above models have disadvantages of weak feature representation ability and not high accuracy when used to process user's historical behavioral data which are quite complicated. Therefore, CNN and RNN-represented deep learning-based prediction models for user purchasing behaviors have been put forward in succession [4,17] . Song et al used user's historical purchasing behavioral data, predicted buyer purchasing time based on MLP and RNN models, respectively, and the results showed that MLP achieved better effect than RNN [18] .…”

Section: Individual Learning Prediction Modelmentioning

confidence: 99%

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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In recent years, with the rapid development of wireless communication network, M-Commerce has achieved great success. Relying on mobile phones, tablets and other wireless communication devices for online shopping has become a mainstream way for users to consume. 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, specifically including: 1) “Sliding window”-centroid under-sampling was combined with sample balance method was used, while the positive sample size was enlarged using “sliding window”, centroid under-sampling was used to reduce the negative sample size within “sliding window”, so as to acquire user’s historical purchasing behavioral data with sample balance. 2) 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. 3) An ensemble learning model—five-fold cross validation stacking—which could be used to predict user purchasing behaviors was raised. Three prediction models—XGBoost-Logistics, LightGBM-L2 and cascaded deep forest models—so that they could realize mutual collaboration and the overall prediction ability of the ensemble learning model could be improved. 4) 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

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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Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior

Cited by 91 publications

References 21 publications

Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites

Log-Based Session Profiling and Online Behavioral Prediction in E–Commerce Websites

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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