Sequential recommendation (SRS) has become a core technique for modern platforms, yet the long-tail distribution of user-item interactions poses persistent challenges. Most users interact sparsely, most items receive little exposure, and existing methods struggle with three issues: (i) collaborative sparsity, where interaction signals collapse in the tail; (ii) limited semantic exploitation, since large language models (LLMs) are mainly used for shallow, point-level embeddings; and (iii) head--tail imbalance, where gains in the tail often come at the cost of head performance. We propose Semantic Alignment with Global Embedding for Rec ommendation (SAGE-Rec ), a new framework that explicitly leverages global semantic organization from LLMs for sequential recommendation. On the item side, SAGE-Rec introduces a fuzzy-membership prototype mechanism that enables tail items to inherit features from semantically related head items. On the user side, it performs alignment and distillation across semantically similar users to enrich sparse representations. At the global level, it applies lightweight regularization to balance semantic and collaborative signals, alleviating the head--tail seesaw effect. Extensive experiments across three real-world datasets and backbone models demonstrate that SAGE-Rec consistently preserves head accuracy while substantially improving recommendations for tail users and items. These results highlight global semantic alignment with LLMs as a principled solution to the long-tail dilemma in sequential recommendation. The implementation code is available for easy reproducibility https://github.com/Applied-Machine-Learning-Lab/WWW2026_SAGE-LLM.

Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that convert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors, which can enhance the recommendation performance. Embedding techniques have revolutionized the capture of complex entity relationships, generating significant research interest. This survey presents a comprehensive analysis of recent advances in recommender system embedding techniques. We examine centralized embedding approaches across matrix, sequential, and graph structures. In matrix-based scenarios, collaborative filtering generates embeddings that effectively model user-item preferences, particularly in sparse data environments. For sequential data, we explore various approaches including recurrent neural networks and self-supervised methods such as contrastive and generative learning. In graph-structured contexts, we analyze techniques like node2vec that leverage network relationships, along with applicable self-supervised methods. Our survey addresses critical scalability challenges in embedding methods and explores innovative directions in recommender systems. We introduce emerging approaches, including AutoML, hashing techniques, and quantization methods, to enhance performance while reducing computational complexity. Additionally, we examine the promising role of Large Language Models (LLMs) in embedding enhancement. Through detailed discussion of various architectures and methodologies, this survey aims to provide a thorough overview of state-of-the-art embedding techniques in recommender systems, while highlighting key challenges and future research directions. To facilitate development, evaluation, and comparison of embedding-based recommender systems, we provide an open source repository ( https://github.com/Applied-Machine-Learning-Lab/Embedding-in-Recommender-Systems ).

Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critically impairing representation learning, especially for long-tail entities. Existing methods also often lack adaptive multi-source signal fusion tailored to item popularity. This paper introduces SPARK, a novel multi-stage framework systematically tackling these issues. SPARK first employs Tucker low-rank decomposition to denoise KGs and generate robust entity representations. Subsequently, an SVD-initialized hybrid geometric GNN concurrently learns representations in Euclidean and Hyperbolic spaces; the latter is strategically leveraged for its aptitude in modeling hierarchical structures, effectively capturing semantic features of sparse, long-tail items. A core contribution is an item popularity-aware adaptive fusion strategy that dynamically weights signals from collaborative filtering, refined KG embeddings, and diverse geometric spaces for precise modeling of both mainstream and long-tail items. Finally, contrastive learning aligns these multi-source representations. Extensive experiments demonstrate SPARK's significant superiority over state-of-the-art methods, particularly in improving long-tail item recommendation, offering a robust, principled approach to knowledge-enhanced recommendation. Implementation code is anonymously online. https://github.com/Applied-Machine-Learning-Lab/SPARK.

Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination. Traditional approaches struggle with distribution discrepancies between heterogeneous features and noise interference in multimodal signals. We propose FindRec (Flexible unified information disentanglement for multi-modal sequential Rec ommendation), introducing a novel ''information flow-control-output'' paradigm. The framework features two key innovations: (1) A Stein kernel-based Integrated Information Coordination Module (IICM) that theoretically guarantees distribution consistency between multimodal features and ID streams, and (2) A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance. Our approach leverages multi-head subspace decomposition for routing stability and RBF-Stein gradient for unbiased distribution alignment, enhanced by linear-complexity Mamba layers for efficient temporal modeling. Extensive experiments on three real-world datasets demonstrate FindRec's superior performance over state-of-the-art baselines, particularly in handling long sequences and noisy multimodal inputs. Our framework achieves both improved recommendation accuracy and enhanced model interpretability through its modular design. The implementation code is available anonymously online for easy reproducibility https://github.com/Applied-Machine-Learning-Lab/FindRec.