Aug 2026· Nexus Science Review· 0 citations· 19 references
TL;DR
LSR retains a general encoder as a transferable semantic substrate while explicit modules reorganize, augment, or index its representations for a defined operational objective.
Abstract
Dense embeddings are foundational to contemporary natural language processing, information retrieval, recommendation, and retrieval-augmented generation. Nevertheless, a single general-purpose vector typically superimposes multiple relations—topic, entailment, sentiment, part–whole structure, evidential role, temporality, and domain-specific constraints—within one geometry and one similarity function. This article presents layered semantic refinement (LSR) as a framework and evaluation protocol rather than as a single universally validated algorithm. LSR retains a general encoder as a transferable semantic substrate while explicit modules reorganize, augment, or index its representations for a defined operational objective. The framework covers learned projections, nonlinear adapters, graph message passing, hierarchical aggregation, deterministic rule channels, clustering, multi-view representations, and prompt-conditioned embeddings.
Semantic Global Graph with Multi-layer Alignment for Recommendation (SeGMA4Rec), which combines item-level semantic enrichment with sequence-level hierarchical regularization, is proposed and achieves strong and generally superior performance over competitive baselines.
DARTopic is a domain-agnostic framework that constructs token-level semantic graphs from frozen PLM embeddings and jointly trains a GNN encoder with topic inference, demonstrating robustness to PLM choice and favorable runtime efficiency over fine-tuning based alternatives.
Results show that adaptively orchestrating foundation-model capabilities can combine scalable retrieval with fine-grained reasoning without task-specific training, and are shown to be state-of-the-art among training-free approaches.
Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar et al.· 0 citations
Data sparsity remains a major obstacle for recommender systems because conventional collaborative filtering methods rely heavily on observed user–item interactions and often fail to exploit richer semantic and relational signals. To address this limitation, we propose SeRel-LightFM, a knowledge-aware hybrid recommendat...
M. Pham, V. Vu, Hung-Nghiep Tran· International Conference on...· 0 citations
PALRec is proposed, a parameter-preserving augmentation framework that equips an LLM with recommendation capabilities while keeping its original parameters fixed and consistently outperforms fully fine-tuned counterparts in recommendation accuracy while preserving the LLM’s pre-trained knowledge.
Hyunsoo Na, Minseok Gang, Sang-goo Lee et al.· ACM Transactions on Informat...· 0 citations
This work introduces Hypergraph-Regularized Gramian Volumes (HyVol), a training-time module that incorporates semantic relations prior to evaluating the original volume loss of Gram, and removes the hypergraph after training, leaving the backbone-only architecture, original scoring function, and retrieval cost unchange...
