RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce

• URL: http://arxiv.org/abs/2109.00724v1
• Date: Thu, 2 Sep 2021 05:38:16 GMT
• Title: RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce
• Authors: Liping Yang, Xiaxia Niu, Jun Wu
• Abstract summary: The number of online payment users in China has reached 854 million. With the emergence of community e-commerce platforms, the trend of integration of e-commerce and social applications is increasingly intense. This paper uses the data-driven method to study the prediction of community e-commerce customers' repurchase behaviour.
• Score: 8.750970436444083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is reported that the number of online payment users in China has reached 854 million; with the emergence of community e-commerce platforms, the trend of integration of e-commerce and social applications is increasingly intense. Community e-commerce is not a mature and sound comprehensive e-commerce with fewer categories and low brand value. To effectively retain community users and fully explore customer value has become an important challenge for community e-commerce operators. Given the above problems, this paper uses the data-driven method to study the prediction of community e-commerce customers' repurchase behaviour. The main research contents include 1. Given the complex problem of feature engineering, the classic model RFM in the field of customer relationship management is improved, and an improved model is proposed to describe the characteristics of customer buying behaviour, which includes five indicators. 2. In view of the imbalance of machine learning training samples in SMOTE-ENN, a training sample balance using SMOTE-ENN is proposed. The experimental results show that the machine learning model can be trained more effectively on balanced samples. 3. Aiming at the complexity of the parameter adjustment process, an automatic hyperparameter optimization method based on the TPE method was proposed. Compared with other methods, the model's prediction performance is improved, and the training time is reduced by more than 450%. 4. Aiming at the weak prediction ability of a single model, the soft voting based RF-LightgBM model was proposed. The experimental results show that the RF-LighTGBM model proposed in this paper can effectively predict customer repurchase behaviour, and the F1 value is 0.859, which is better than the single model and previous research results.

Related papers

• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning [20.412469498888292]
  Federated Learning (FL) enables multiple devices to collaboratively train a shared model. The selection of participating devices in each training round critically affects both the model performance and training efficiency. We introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach.
  arXiv  Detail & Related papers  (2024-05-07T08:44:29Z)
• Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization [30.305956110710266]
  Federated learning is beneficial for enabling the training of models across distributed clients. Existing work has overlooked the coexistence of model training and inference under clients' limited resources. This paper focuses on the joint optimization of model training and inference to maximize inference performance at clients.
  arXiv  Detail & Related papers  (2023-12-20T09:27:09Z)
• Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data [14.948017876322597]
  This paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our approach integrates customer sentiments financial literacy (FL) level, and financial behavioral data. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54.
  arXiv  Detail & Related papers  (2023-12-03T06:28:55Z)
• A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction [3.6002910014361857]
  Customer Lifetime Value (CLV) is the total monetary value of transactions/purchases made by a customer with the business over an intended period of time. CLV finds application in a number of distinct business domains such as Banking, Insurance, Online-entertainment, Gaming, and E-Commerce. We propose a system which is able to qualify both as effective, and comprehensive yet simple and interpretable.
  arXiv  Detail & Related papers  (2023-08-07T14:22:02Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• 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)
• Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling [58.14878401145309]
  We develop a novel approach to producing more sample-efficient estimators of expectations in the PL model. We illustrate our findings both theoretically and empirically using real-world recommendation data from Amazon Music and the Yahoo learning-to-rank challenge.
  arXiv  Detail & Related papers  (2022-05-12T11:15:47Z)
• Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic [67.00475077281212]
  Model-based reinforcement learning algorithms are more sample efficient than their model-free counterparts. We propose a novel approach that achieves high sample efficiency without the strong reliance on accurate learned models. We show that CMBAC significantly outperforms state-of-the-art approaches in terms of sample efficiency on several challenging tasks.
  arXiv  Detail & Related papers  (2021-12-16T15:33:11Z)
• Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression [0.0]
  We provide new predictive analytics of customer churn rate based on a machine learning method that enhances the classification of logistic regression by adding a mixed penalty term. We show the analytical properties of the proposed method and its computational advantage in this research.
  arXiv  Detail & Related papers  (2021-05-17T08:40:34Z)
• Model-Augmented Q-learning [112.86795579978802]
  We propose a MFRL framework that is augmented with the components of model-based RL. Specifically, we propose to estimate not only the $Q$-values but also both the transition and the reward with a shared network. We show that the proposed scheme, called Model-augmented $Q$-learning (MQL), obtains a policy-invariant solution which is identical to the solution obtained by learning with true reward.
  arXiv  Detail & Related papers  (2021-02-07T17:56:50Z)

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.