User Purchase Behavior Research Articles

Understanding the effect of TikTok marketing on user purchase behavior: a mixed-methods approach

Understanding the effect of TikTok marketing on user purchase behavior: a mixed-methods approach

Prediction Model of E-commerce Users' Purchase Behavior Based on Deep Learning

This study mainly uses Deep Learning (DL) technology to build a prediction model of e-commerce users' purchasing behavior, and evaluates its application effect on actual e-commerce data. Methodologically, Recurrent Neural Network (RNN) is used to capture the temporal dependence of user behavior, and the performance of the model is improved through detailed data preprocessing and feature engineering. The research results show that the DL model based on RNN has achieved remarkable advantages in predicting the purchase behavior of e-commerce users. Compared with other methods, RNN model can capture the temporal dependence of user behavior more accurately, thus improving the prediction accuracy. This advantage is especially obvious when dealing with complex and dynamic user behavior data. It provides new ideas and methods for accurate marketing and personalized service of e-commerce industry.

Personalized Cadence Awareness for Next Basket Recommendation

This empirical study addresses the problem of Next Basket Repurchase Recommendation (NBRR), an often overlooked aspect of Next Basket Recommendation (NBR). While NBR aims to suggest items for a user’s next basket based on their prior basket history, NBRR focuses solely on recommending items previously purchased by the user. Despite the common ground between NBR and NBRR, the latter requires a distinct approach. In this paper, we survey recent developments in the fields of NBR and NBRR, emphasizing the different strategies employed for these closely related challenges. In addition, we review the common characteristics of users’ repurchase patterns, which characterize the NBRR problem. Building on these insights, we introduce a novel hyper-convolutional model tailored to capture behavioral patterns associated with repeated purchases. To evaluate its effectiveness, we conduct experiments on three publicly available datasets, offering a comprehensive analysis across three levels of granularity: user-level, order-level, and item-level. Our analysis illuminates the conditions under which the model excels and identifies scenarios where it may encounter challenges. This research contributes valuable insights into enhancing repurchase recommendation systems and advancing the understanding of user purchase behavior in general.

Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling.

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.

Factors influencing TikTok-based user purchase intention: comparison between potential customers and repeat customers

PurposeThe purpose of this paper is to examine how different types of user experience in TikTok impact purchase intention via commitment to the influencer and commitment to the platform, with customer type included to determine moderating effects. Three types of user experience are considered: information experience, entertainment experience and parasocial-relationship-based experience.Design/methodology/approachThis study collected 458 valid questionnaires from TikTok users, employing the structural equation modeling approach to examine the proposed research model.FindingsInformation experience, entertainment experience and parasocial-relationship-based experience are found to critically stimulate user commitment to the influencer and commitment to the platform, in turn driving TikTok-based purchase intention. Tests incorporating customer type reveal that commitment to the influencer more strongly influences the purchase intention of repeat customers, with commitment to the platform more likely to stimulate purchase intention among potential customers.Research limitations/implicationsOn a theoretical level, the paper is among the first to examine TikTok-based user purchase intention with customer type as a moderator. On a practical level, the results can guide marketers to effectively promote products using TikTok and inspire TikTok managers to develop customized strategies to stimulate initial and repeat sales.Originality/valueTikTok is moving to the stage of commercialization and monetization by introducing e-commerce features. Although this move should cultivate particularly fertile ground for companies to sell products, TikTok user purchase behavior has yet to receive sufficient research attention, with little currently known about their purchase motivations. The current study uncovers the significant antecedents of users' purchase intention through TikTok, and further reveals the motivational differences among potential and repeat customers.

A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.

A User Purchase Behavior Prediction Method Based on XGBoost

With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature behavior fusion is carried out to generate the user tag feature according to user behavior patterns. Next, the XGBoost feature importance model is employed to analyze multi-dimensional features and identify the model with the most significant weight value as the key feature for constructing the model. This feature, together with other user features, is then used for prediction via the XGBoost model. Compared to existing machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), the eXtreme Gradient Boosting (XGBoost) model outperforms with an accuracy of 0.9761, an F1 score of 0.9763, and a ROC value of 0.9768. Thus, the XGBoost model demonstrates superior stability and algorithm efficiency, making it an ideal choice for predicting user purchase behavior with high levels of accuracy.

Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns

Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.

The influence of user cognition on consumption decision-making from the perspective of bounded rationality

Consumer decision-making is related to the success or failure of enterprises, and products that cater to the cognitive preferences of users have become a focus of current research. Based on the theory of bounded rationality, this paper explores the cognitive process of consumer decision-making. Then, how product shape affects consumption decision-making is analyzed with eye-tracking technology. Finally, the design principle of the product form is further explored. The results demonstrate the following: (1) the perceptual cognition of users has a driving effect on consumption behavior; (2) as a key factor affecting the perceptual cognition of users, product form affects consumer decision-making by influencing the degree of approach motivation; (3) by establishing the mapping relationship between product form elements and user images, the principles of product form design can be more consistent with user image preferences. This study provides useful suggestions for how to increase the purchase behavior of users from the perspective of bounded rationality.

A Deceptive Reviews Detection Method Based on Multidimensional Feature Construction and Ensemble Feature Selection

Deceptive reviews on social media and e-commerce websites are inflammatory and will significantly affect the judgment and purchase behavior of other users. At present, many researchers build models based on single text features to detect deceptive reviews. However, deceptive reviewers will deliberately imitate the text style of true reviews when writing reviews. At this time, these methods based on text features are not necessarily effective. What’s more, detection performance is limited because the category distribution is likely to be unbalanced in practice. In this work, to address these shortcomings, a deceptive review detection method based on multidimensional feature construction and ensemble feature selection is proposed. Our proposal constructs 3-D features including text feature, reviewer behavior feature, and deceptive score feature. In addition, to alleviate the impact of unbalanced category distribution, a data resampling algorithm is applied which incorporates random under-sampling (RUS) and Borderline-SMOTE algorithm. Furthermore, we integrate the results of different feature selection based on the Chi-square test, Information gain, and XGBoost feature importance. Our method addresses the limitation of single dimension features and can provide useful detection. Experimental results area under the curve (AUC, Macro Average Precision, and weighted F1-score) show that the proposed method performs well in the task of deceptive review detection on two Amazon datasets. Compared with other advanced methods, our method achieves additional performance gains in the case of poor text quality and datasets with unbalanced category distribution.

Bibliometric Analysis: Consumers Interest in E-Commerce using VOSviewer

Purpose: The focus of this research is to evaluate the trend of consumer interest in e-commerce in the last 10 years using data accessed from the Scopus online database. Design/methodology/approach: This study measures bibliometric analysis of 17,988 articles from 366 journals during 2013-2022. Findings: The findings show that the trend of publications about consumer interest continues to increase. The most cited article is Effect of social commerce factors on user purchase behavior: An empirical investigation from renren.com with 150 citations. The country with the highest number of publications is China with 90 publications with 732 citations. Research limitations/implications: This study has limitations on the database which is only sourced from the Scopus database. Originality/value: This study makes the most influential contribution and impacts to research on emerging consumer interests in various fields, including leading researchers and their country of origin.

A Novel Approach for Product Recommendation Using Smartphone Sensor Data

Human Activity-based studies have become an omnipresent research topic in Machine Learning. Considering the countless impacts of human activity on persons' everyday life, we have analyzed the correlation between human activity and their product preferences in our study and proposed that daily human activity could be a metric for product recommendation models. To address this previously unaccounted phenomenon, a new approach is presented in our study that gives real-time recommendations to users by observing their activeness in daily life. However, product recommendation systems mostly believe in ratings, and the purchase behavior of users instead of investigating the precious insights of users' daily activities. But we examined smartphones' GPS sensor data using machine learning algorithms to urge insights from users' daily activeness and proposed a model for predicting the product of interest of the purchasers, based on the activeness of their daily life. Moreover, based on our model, we have introduced a prototype of a real-time recommendation system, especially for the retail shops that rely on users' implicit data from smartphone sensors to form product recommendations. For conducting our study, we developed an android application that—collects embedded smartphone sensor data and can detect objects to provide product recommendations and product details. Experiment shows, that our proffered daily activeness-based recommendation system using smartphone sensor data, performs with a precision of 66%, but it is also a promising performance because it does not use customers' explicit feedback.

A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data

In order to develop a more efficient and accurate marketing strategy for consumers’ purchase behavior, this paper establishes a user value model by modeling and learning the user historical data of e-commerce enterprises. The improved K-means algorithm is used to cluster the purchase behavior of users, and the customer value matrix is constructed from two dimensions of consumption frequency and average consumption amount. Finally, e-commerce users are classified into four categories by marking points. The test results show that the improved K-means algorithm is stable and efficient, and the analysis of user clustering characteristics is helpful to develop more accurate marketing strategies.

Prediction of Online Consumers’ Repeat Purchase Behavior via BERT-MLP Model

It is an effective means for merchants to carry out precision marketing and improve ROI by using historical user behavior data obtained from promotional activities in order to build a model to predict the repeat purchase behavior of users after promotional activities. Most of the existing prediction models are supervised learning, which does not work well with a small amount of labeled data. This paper proposes a BERT-MLP prediction model that uses “large-scale data unsupervised pre-training + small amount of labeled data fine-tuning.” The experimental results on Alibaba real dataset show that the accuracy of the BERT-MLP model is better than the baseline model.

Mediating Role of Consumer Involvement in the Relationship between Marketing Stimuli and Consumer Purchase Behavior

This study has applied the claims of the S-O-R model regarding consumer purchase behavior and investigates the impact of marketing stimuli on the purchase behavior of Smartphone users. To add value to the concept; researchers have conceptualized consumer involvement as a mediator in the relationship between marketing stimuli and consumer purchase behavior. Marketing stimuli are external marketing components and include factors such as product characteristics, pricing, processes, personnel, tangible evidence, and promotion. In this connection, a quantitative and deductive approach has been employed to conduct cross-sectional primary data. To underline the phenomenon, a sample of 425 smartphone users has responded through a self-administered questionnaire. Based on theory and prior literature, hypotheses are generated in order to test through AMOS. In this regard, a measurement model was established while setting all the thresholds of the model. Finally, a structural model was then tested with the aim to test the hypotheses. The findings of the study revealed that marketing stimuli have a positive impact on both consumer involvement and purchase behavior. Whereas consumer involvement does mediate the relationship between marketing stimuli and consumer purchase behavior. This research provides a deep insight into the use of marketing stimuli in studying consumers’ purchase behavior. Further research can be conducted by observing the impact of various social-culture aspects on customer behavior, also, different marketing factors, such as promotion, people, venue, and procedures, could be used as dimensions of marketing stimuli to increase the study's exposure and yield diverse outcomes.

Facebook effects on envy, depression, and impulse buying toward university students in Ho Chi Minh City

This study combines the theoretical framework of social ranking and envy, depression and impulse purchase behavior of Facebook users. The purpose of the study is to determine the relationship between Facebook, envy, depression and impulsive buying. With the combination of quantitative and qualitative methods conducted with 380 observed samples who are university students in Ho Chi Minh City frequently browsing Facebook, research results show that regular use of Facebook causes envy and depression. To alleviate this negative psychology, students tend to buy impulsively, though this behavior causes financial and mental harm to the young buyers. The study applies basic linking to online interactions, especially on Facebook. College students spend a large amount of time on online activities, especially accessing Facebook. Research results show that despite the pleasure given by using Facebook, the platform’s users will suffer from depression caused by long hours spent. If college students are aware of the potential dangers of overusing their time on Facebook, they can limit their use or become more aware of negative feelings generated by envy about others' personal information. Successfully avoiding depression requires college students to retain a positive perception and keep an optimistic attitude.

Gamblified digital product offerings: an experimental study of loot box menu designs

To augment traditional monetization strategies, digital platform providers increasingly draw on gamblification (i.e., the use of gambling design elements). By means of gambling design elements (e.g., lottery tickets, scratch cards, loot boxes), platform providers do not only entertain users but also incentivize them to purchase digital products. Yet, despite the increasing prevalence of gamblified digital platforms, little is known about how gamblification influences user purchase behaviors. Drawing on prospect theory, we investigate gamblification in the form of loot box menu designs and the associated effects of uncertainty, loss experience and behavioral control on user purchase behavior. Specifically, we conducted a contest-based online experiment with 159 participants, finding that platform providers can profit from offering loot boxes with certain (vs. uncertain) rewards in loot box menus. Furthermore, this effect intensifies when participants previously experienced a loss and decreases when they perceive to have more control over the result. Thus, our findings provide theoretical and practical insights for a better understanding of gamblification in general and of loot box menu designs for enhancing digital business models in particular.

RETRACTED ARTICLE: Intelligent recommendation method integrating knowledge graph and Bayesian network

In recent years, the amount of Internet information data has exploded, and the problem of “information overload” has become a huge challenge for Internet development. Through the prior knowledge of the known data distribution, combined with sample training data to estimate the mathematical model of the overall data. Intelligent recommendation methods based on knowledge graphs and Bayesian networks are a hot spot in the current Internet research, and they are of great significance to search engines and e-commerce. This paper studies the fusion of knowledge graph and Bayesian belief network and its application in personalized recommendation and designs a Bayesian network that describes the purchase behavior of users based on the purchase records of customers across the network. User recommendation is the process of fusing knowledge graphs and Bayesian network reasoning. For the design of the recommendation network, a random reasoning algorithm with approximate reasoning is selected in consideration of calculation scale and accuracy. This paper uses the content filtering technology of KNN algorithm to supplement the sparse matrix, which can improve the quality of collaborative filtering recommendation, thereby improving the quality of final recommendation. In this paper, based on the knowledge graph, the EPCA algorithm and the robust recommendation algorithm are merged to form a robust collaborative recommendation model based on Bayesian probability matrix decomposition. The robust recommendation algorithm further improves the robustness of the recommendation algorithm by introducing the suspected users and target items detected by the EPCA algorithm. Research shows that the PS value of the VarSelect SVD algorithm used in this paper is approximately between 0.36 and 1.04. Compared with the first two algorithms, the PS value of VarSelect SVD has been significantly reduced, and the algorithm robustness has been significantly improved.

Research on prediction of online purchasing behavior based on hybrid model

The research on the potential purchase behavior of users can help merchants develop better marketing strategies. At present, many research methods of online purchasing behavior are based on simple rule prediction, and the prediction results are not satisfactory. We design a hybrid model of Gradient Boosting Decision Tree and logistic regression to accurately predict the purchase behavior of users, which combines the association characteristics between users and commodities. Firstly, clustering algorithm and association rules are used to solve the problem of data imbalance and mine more potential related features. This scheme not only improves the processing efficiency of large data, but also solves the problem of user cold start. Secondly, we construct a scalable tree enhancement system (XGBoost) to train the initial feature set, which is a strong classifier composed of several weak classifiers. A new training set combines the new features with the original features through feature reconstruction, and a hybrid machine learning system is constructed by logistic regression (LR) model. Finally, the LR model is trained by the new training set. Compared with the existing schemes, the integrated decision tree model can train more sample sets with less resources. The experimental results show that the accuracy of the hybrid model is better than single model, and the F1_score is higher.

SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning.

Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design, which are important to improving the performance of machine learning algorithms. In this paper, we proposed an SE-stacking model based on information fusion and ensemble learning for user purchase behavior prediction. After successfully using the ensemble feature selection method to screen purchase-related factors, we used the stacking algorithm for user purchase behavior prediction. In our efforts to avoid the deviation of the prediction results, we optimized the model by selecting ten different types of models as base learners and modifying the relevant parameters specifically for them. Experiments conducted on a publicly available dataset show that the SE-stacking model can achieve a 98.40% F1 score, approximately 0.09% higher than the optimal base models. The SE-stacking model not only has a good application in the prediction of user purchase behavior but also has practical value when combined with the actual e-commerce scene. At the same time, this model has important significance in academic research and the development of this field.