AHCI RESEARCH GROUP

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2025

Lakehal, A.; Alti, A.; Annane, B.

CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences Journal Article

In: Future Internet, vol. 17, no. 2, 2025, ISSN: 19995903 (ISSN).

Abstract | Links | BibTeX | Tags: Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping

@article{lakehal_cores_2025,

title = {CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences},

author = {A. Lakehal and A. Alti and B. Annane},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218626299&doi=10.3390%2ffi17020094&partnerID=40&md5=a0f68e273de08b2c33d03da4cb6c19bb},

doi = {10.3390/fi17020094},

issn = {19995903 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Future Internet},

volume = {17},

number = {2},

abstract = {In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. © 2025 by the authors.},

keywords = {Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping},

pubstate = {published},

tppubtype = {article}

}