The long-tail recommendation problem remains a significant challenge in modern recommender systems, primarily due to data sparsity and popularity bias, which hinder the accurate ID representation of users and items. Recent advancements in large language models (LLMs) have enabled the direct modeling of user and item semantic representations, offering potential improvements in representation learning through the alignment of these two types of representations. However, systems relying on LLM representation alignment face two critical challenges: (1) the substantial differences between LLMs and recommendation models in terms of training objectives, phases, and data; (2) the pervasive popularity bias in collaborative data. These challenges create a semantic gap between ID representations and semantic representations. Directly aligning these representations risks introducing recommendation-irrelevant noise, disrupting the collaborative information embedded in ID representations, and ultimately leading to suboptimal recommendation outcomes. To address this gap, we propose DeltaRec, aDouble-enhancement framework forlong-tailRecommendation. DeltaRec tackles the long-tail recommendation problem through two approaches. First, it incorporates semantic information for all items. Second, it provides additional supervision signals specifically for long-tail items. The framework begins by disentangling ID representations into interest representations and conformity representations. To integrate semantic information from LLMs while preserving popularity information, we design a contrastive learning-based semantic alignment module that aligns interest representations with semantic representations. Furthermore, to enhance the representation learning of unpopular items, we introduce a ranking-based behavior alignment module, which provides additional supervision signals for these items. To avoid introducing recommendation-irrelevant noise and disrupting collaborative semantics due to excessive alignment, we propose a curriculum learning-based training mechanism. Extensive experiments on real-world datasets demonstrate that DeltaRec effectively mitigates popularity bias and significantly improves long-tail recommendation performance without relying on prior knowledge of popularity distributions. Our code is available athttps://github.com/leo0481/DeltaRec/E3D7.

Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update representations dynamically, existing studies have investigated event-level and history-level dynamics by modeling the newly arrived interactions and aggregating historical interactions, respectively. However, most of them directly learn the representation evolution as new interactions occur, without exploring the collaboration between the newly arrived and historical interactions, thus failing to scrutinize whether those new interactions would benefit the evolution learning process when generating dynamic representations. Moreover, most of them model the two levels of dynamics independently, explicitly ignoring the inherent co-evolving correlation between them. In this work, we propose the Temporal Collaboration-Aware Graph Co-Evolution Learning (TCGC) for the dynamic recommendation scenario. First, we explore the effectiveness of collaborative information and devise the collaboration-aware indicator to guide the evolution learning process. Second, we design a temporal co-evolving graph network, enabling our framework to capture the correlation between event and history dynamics. Third, we leverage the evolution task and recommendation task together for joint training. Extensive experiments on four public datasets demonstrate the superiority and effectiveness of our proposed TCGC.