Jul 2026· ACM Transactions on Information Systems· Vol 44, pp. 1-32· 0 citations· 75 references
TL;DR
The proposed REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration, demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.
Abstract
The integration of Large Language Models into recommender systems has introduced a new paradigm in which models leverage their pre-trained knowledge to generate recommendations. A prevailing assumption is that an LLM’s inherent knowledge is sufficient to support high-quality recommendations across diverse domains. This paper challenges that assumption, positing that in specialized domains, recommendation efficacy is limited by the textual nature of an LLM’s knowledge. To address this limitation, we propose REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration. Central to our approach is the extension of established text-conversion techniques to non-traditional data modalities. We utilize knowledge lexicalization, a process that translates heterogeneous data sources into a unified natural language format. This lexicalized corpus is then used in a knowledge-aware instruction-tuning pipeline to explicitly align the LLM’s internal representations with four distinct types of domain-specific information. We conduct experiments across four distinct domains to validate our framework. Our findings provide consistent empirical evidence that while an LLM’s inherent knowledge may suffice for universally familiar domains like movies, recommendation quality in more specialized areas is significantly improved through knowledge integration. The proposed approach demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.
This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.
Alexander Chen, Caroline Tang, Jennifer Sleeman· 0 citations
LMEnt is released to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, hallucinations, and learning dynamics, finding that entity co-occurrence and mention forms—which are difficult to study with existing tools—affect learning trends.
Daniela Gottesman, Alon Gilaie-Dotan, Ido Cohen et al.· Transactions of the Associat...· 0 citations
Integrating newly emerging terms into existing ontologies is a recurring maintenance problem in knowledge engineering, particularly in biomedical domains where terminology evolves faster than manual curation can accommodate. This paper addresses the placement of concepts that are absent from the target ontology—the out-of-knowledge-base setting—in which a textual mention must be assigned one or more insertion positions in the subsumption hierarchy rather than linked to an existing node. We propose a three-stage framework that extends the conventional retrieve-then-select paradigm with an intermediate stage of edge generation and enrichment, which expands the candidate set by traversing the local structure of the ontology. Stage 1 retrieves candidate edges using a fine-tuned bi-encoder trained with a max-margin objective; Stage 2 constructs and structurally enriches candidate edges; Stage 3 selects among them using either a fine-tuned cross-encoder or a large language model under explainable instruction tuning. We evaluate on two datasets derived from SNOMED CT, MM-S14-Disease and MM-S14-CPP, under a strict out-of-knowledge-base protocol. Fine-tuned pre-trained language models outperform zero-shot and instruction-tuned large language models on ranking accuracy, while the instruction-tuned configuration produces expert-auditable justifications at a modest cost in accuracy. On MM-S14-Disease, the strongest configuration places a correct insertion edge among the ten highest-ranked candidates for 38.7% of test mentions and recovers the complete gold edge set for 16.4%, against 26.1% and 9.2% for retrieval alone. The framework is positioned as decision support for ontology curators rather than as an autonomous ontology generator.
Zhanna B. Sadirmekova, M. Sambetbayeva, B. Abdygalym et al.· Information· 0 citations
Traditional sequential recommender systems have primarily relied on implicit pattern recognition in user interaction sequences, achieving strong performance but functioning as “black boxes” that lack transparent reasoning. This paper introduces LLM-RS, a novel framework that leverages Large Language Models to enable explicit reasoning chains in sequential recommendation. Our approach transforms the recommendation task from mere pattern matching to interpretable reasoning by developing a multi-stage architecture that: (1) extracts structured preference profiles from user interaction sequences, (2) generates explicit reasoning chains analyzing candidate items against inferred preferences, and (3) produces persuasive explanations alongside recommendations. We propose three model variants—fine-tuned reasoning, retrieval-augmented generation, and hybrid ensemble—that integrate LLM-based reasoning with traditional collaborative filtering. The framework addresses key challenges in modern recommender systems by providing transparent, persuasive rationales while maintaining competitive performance, marking a significant step toward more interpretable and trustworthy recommendation systems. Comprehensive evaluations across the Amazon Reviews, MovieLens, MIND, and KuaiSAR datasets demonstrate that LLM-RS not only matches state-of-the-art methods in accuracy but also significantly enhances explanation quality, user trust, and recommendation diversity. Our findings reveal that reasoning-enabled recommendations increase user adherence in online experiments and improve long-term engagement metrics.
A. Gadallah, H. Hefny, Mohammed E. Almandouh et al.· Mathematics· 0 citations
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated independently for each candidate triple and may violate fundamental relational constraints such as transitivity, symmetry, and functional uniqueness, leading to contradictory and unreliable outputs. We propose CONSISTRE, a unified consistency-aware framework for DocRE that addresses this limitation through two complementary tracks. The first operates at inference time for black-box LLMs, combining constraint-aware prompting, constraint-based verification, and iterative self-reflection to refine predictions without task-specific fine-tuning. The second injects consistency knowledge into smaller open-source models via a knowledge distillation and reinforcement learning pipeline: reasoning traces from a powerful teacher are distilled into a student via supervised fine-tuning, followed by GRPO alignment using a composite reward that jointly optimizes extraction performance and relational consistency. Together, the two tracks cover both API-accessible and locally deployable scenarios under a unified consistency formulation. Experiments on DocRED show that both tracks outperform their baselines, with the inference-time track achieving competitive F1 using off-the-shelf black-box LLMs and the training-time track substantially narrowing the gap between 7--8B open-source models and state-of-the-art proprietary LLMs at a fraction of their inference cost. Ablation studies confirm that explicit consistency modeling mitigates relational contradictions and enhances the reliability of LLM-based DocRE across both deployment paradigms.