CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction

• URL: http://arxiv.org/abs/2505.13558v1
• Date: Mon, 19 May 2025 09:07:34 GMT
• Title: CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction
• Authors: Yingjie Kuang, Tianchen Zhang, Zhen-Wei Huang, Zhongjie Zeng, Zhe-Yuan Li, Ling Huang, Yuefang Gao,
• Abstract summary: Predicting whether a customer will make the next purchase is a classic time series forecasting task.<n>Customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers.<n>This paper proposes a unified Clustering and Attention GRU model that leverages multi-modal data for customer purchase intention prediction.
• Score: 7.294680030004759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately predicting customers' purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase is a classic time series forecasting task. However, in real-world purchasing behavior, customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers. This head-to-tail distribution makes traditional time series forecasting methods face certain limitations when dealing with such problems. To address the above challenges, this paper proposes a unified Clustering and Attention mechanism GRU model (CAGRU) that leverages multi-modal data for customer purchase intention prediction. The framework first performs customer profiling with respect to the customer characteristics and clusters the customers to delineate the different customer clusters that contain similar features. Then, the time series features of different customer clusters are extracted by GRU neural network and an attention mechanism is introduced to capture the significance of sequence locations. Furthermore, to mitigate the head-to-tail distribution of customer segments, we train the model separately for each customer segment, to adapt and capture more accurately the differences in behavioral characteristics between different customer segments, as well as the similar characteristics of the customers within the same customer segment. We constructed four datasets and conducted extensive experiments to demonstrate the superiority of the proposed CAGRU approach.

Related papers

• TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting [51.37167759339485]
  We propose Temporal-Aligned Transformer (TAT), a multi-horizon forecaster leveraging apriori-known context variables for improving predictive performance.<n>Our model consists of an encoder and decoder, both embedded with a novel Temporal Alignment Attention (TAA) designed to learn context-dependent alignment for peak demand forecasting.<n>We demonstrate that TAT brings up to 30% accuracy on peak demand forecasting while maintaining competitive overall performance compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-14T14:51:24Z)
• Customer Analytics using Surveillance Video [0.0]
  This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns.<n>An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest.
  arXiv  Detail & Related papers  (2025-03-01T11:26:31Z)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms.<n>Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction.<n>We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning [50.060154488277036]
  In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness.<n>We propose two guiding principles that tackle the inherent conflict between the two metrics while reinforcing each other.<n>Our approach adaptively enhances this diversity by selecting clients based on their data distributions, thereby improving both model performance and fairness.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• Modelling customer churn for the retail industry in a deep learning based sequential framework [0.0]
  This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. We are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour.
  arXiv  Detail & Related papers  (2023-04-02T16:48:43Z)
• Customer Churn Prediction Model using Explainable Machine Learning [0.0]
  Key objective of the paper is to develop a unique Customer churn prediction model which can help to predict potential customers who are most likely to churn. We evaluated and analyzed the performance of various tree-based machine learning approaches and algorithms. In order to improve Model explainability and transparency, paper proposed a novel approach to calculate Shapley values for possible combination of features.
  arXiv  Detail & Related papers  (2023-03-02T04:45:57Z)
• A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study [2.126171264016785]
  We develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools.
  arXiv  Detail & Related papers  (2022-12-01T19:37:29Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• On the Convergence of Clustered Federated Learning [57.934295064030636]
  In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns. This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
  arXiv  Detail & Related papers  (2022-02-13T02:39:19Z)
• OPAM: Online Purchasing-behavior Analysis using Machine learning [0.8121462458089141]
  We present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters.
  arXiv  Detail & Related papers  (2021-02-02T17:29:52Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.