A New Explainable-AI Recommendation Paper Offers Merchants a Research Signal, Not a Playbook

The record lists “An Explainable AI-Based Recommendation System for E-Commerce Platforms,” authored by Shalini M. R. and Nayana K., with a 2026 publication year.

The work is listed in the International Journal of Innovative Science and Research Technology; its publication status is unknown, not confirmed as peer-reviewed.

The record carries DOI 10.38124/ijisrt/26aug580, OpenAlex ID W7204141275, and source item ID W7204141275.

The available evidence is bibliographic metadata only: no abstract, body text, PDF, quotations, methods, sample details, or results are retained.

Explainability is commercially relevant because merchants may eventually need to understand or communicate why a recommendation system presents particular products, but this record does not establish how the named system works.

The title points to a potential connection between AI recommendations and e-commerce merchandising; the evidence does not show whether the work is deployable, effective, or relevant to a specific commerce platform.

For operators evaluating recommendation technology, the main near-term value is discovery: the DOI and OpenAlex ID provide stable identifiers for later verification rather than evidence for an immediate implementation decision.

Sellers should not change recommendation logic, merchandising rules, or product-ranking workflows based on this record alone.

Teams assessing AI commerce vendors can use the paper’s title and identifiers as a research-tracking entry, while requiring verifiable technical and performance documentation before procurement.

If explainable recommendations become a priority, merchants may need requirements for reason displays, human review, data governance, and auditability; this source does not specify any of those requirements.