KALEFormer a transformer based model integrating KMeans clustering and LDA for market basket analysis
Year: 2026
Type: article
Abstract: The highly sparse and dynamic nature of e-commerce transactions presents a significant challenge for accurately predicting customer intent and composition of future purchases. While traditional Market Basket Analysis (MBA) and collaborative filtering models provide foundational insights, they often fail to integrate preference history, semantic and propensity features that could provide useful insights into user purchase behaviour. To address this, this study proposes KALEFormer (KMeans And LDA Enriched Transformer for Market Basket Analysis), an integrated framework that combines machine learning, natural language processing and deep learning to enhance predictions. Experiments are conducted on the UCI Online Retail dataset. Machine learning techniques are first used for data preprocessing, dimensionality reduction via PCA and customer segmentation via KMeans clustering. Latent thematic information is then captured by topic modelling on item descriptions via LDA and semantic item representations are derived using MiniLM sentence embeddings. These enriched signals are subsequently fused using an encoder-only transformer model to learn basket-level purchasing patterns. The objective is to enhance recommendation by jointly modelling customer tendencies and semantic context rather than relying solely on item co-occurrence. Evaluation results compared to state of the art models demonstrate that KALEFormer performs effectively even in a sparse recommendation, achieving the highest metrics: HR@k of 80.69% & 90.54% and NDCG@k of 38.01% & 35.16% for k = 10 and 50 respectively. Additional train–test analysis indicates stable generalization and ablation test confirms that the inclusion of contextual metadata features contributes to significant performance improvements.
Source: Discover Computing
Authors: Ishan Surana, Manjula C. Belavagi, Ramyashree
Institution: Manipal Academy of Higher Education
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7155170320