In real-world scenarios, users tend to engage with a small set of popular items, while a large number of long-tail items receive little to no interaction. This long-tail phenomenon substantially impairs recommendation quality. Although prior approaches have attempted to address this issue, the absence of sufficient collaborative signals remains a major obstacle. With the advent of Large Language Models (LLMs), recent studies have explored leveraging LLM-derived semantics to enrich recommendation models. These approaches aim to incorporate textual or contextual knowledge to compensate for limited user-item interactions. A key challenge, however, lies in effectively integrating semantic signals with collaborative representations, which originate from different modalities and learning dynamics. To tackle this, We propose a novel framework, called FCRLLM (the Flipped Classroom with LLM), for long-tail sequential recommendation that aligns collaborative and LLM-based semantic representations. The flipped classroom mechanism dynamically updates the teacher representation to align with the student's attention, enabling more effective integration of semantic and collaborative information. This alignment is implemented via an energy-based formulation inspired by Hopfield networks. To validate its effectiveness, we conduct extensive experiments on three real-world datasets and demonstrate that FCRLLM consistently improves recommendation performance regardless of item popularity or user activity.

Large Language Models (LLMs) excel in various NLP tasks but remain underexplored in recommendation systems. This study proposes the Sequential Emotion-Aware LLM-Based Personalized Recommendation System (SEALR ) to leverage sentiment analysis in user-generated reviews, tracking emotional changes and extracting sentiment labels. It integrates candidate items produced by sequential models with user behavior data into an LLM, enhancing personalization. Experiments on Amazon and Yelp datasets explore the effect of varied candidate pool sizes and instruction-based fine-tuning ratios, demonstrating significant performance gains. The combination of sentiment insights and user behavior data effectively accommodates diverse user preferences and contexts.