A Hybrid Predictive Matrix Framework for Alleviating Sparsity in Digital Commerce Indices

International Journal of Computer Science and Technology Vol. 1, No. 7, July 1999 | pp. 52–60

DOI: 10.46882/1999/IJCST/000007

Research Article

Title: A Hybrid Predictive Matrix Framework for Alleviating Sparsity in Digital Commerce Indices

Names of Authors: Michael Brown¹, Sun-Woo Kim²

Authors’ Affiliations: ¹School of Computer Science, University of Manchester, UK; ²Department of Computer Engineering, Seoul National University, South Korea.

Abstract:
E-commerce retail channels depend on information filtering layers to connect buyers with relevant catalog listings. Standard correlation algorithms often fail when encountering severe cold-start parameters or limited historical transaction databases. This paper presents a novel hybrid prediction architecture that integrates user metadata descriptors with explicit rating vectors through an interconnected matrix layer. The model uses dense matrix embeddings to convert sparse categorical variables into a shared lower-dimensional operational layer, identifying complex connections between buyer behavior and catalog features. The hybrid model was tested using a public benchmark dataset containing 10 million data points. The outcomes show an overall Root Mean Squared Error (RMSE) reduction of 6.8% compared to baseline collaborative models, along with a 12.4% precision improvement for newly added catalog entries. The architecture displays strong resilience against data scarcity, maintaining accurate matches even when user profiles contain fewer than five history entries. This research proves that combining user profiles with item attributes resolves systemic data sparsity limitations.

Keywords: Recommender Paradigms, Collaborative Filtering, Matrix Factorization, Data Sparsity, Information Extraction, Retrieval Engines.

Manuscript Timeline: Received: April 02, 1999; Revised: May 15, 1999; Accepted: June 08, 1999; Published: July 20, 1999.

Citation: Brown, M., & Kim, S. W. (1999). A Hybrid Predictive Matrix Framework for Alleviating Sparsity in Digital Commerce Indices. International Journal of Computer Science and Technology, 1(7), 52–60.

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