Azambuja, Rogério Xavier deMorais, A. JorgeFilipe, Vítor Manuel Jesus2026-02-262026-02-262025-07-16de Azambuja, R. X., Morais , A. J., & Filipe, V. (2025). A Framework for Adaptive Recommendation in Online Environments. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA52025020http://hdl.handle.net/10400.2/21545Recent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users’ dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic.engRecommender systemsDynamic taste profileDeep neural networksTransformersAttention modeljournal article10.47852/bonviewaia520250202811-0854