Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)

• URL: http://arxiv.org/abs/2603.03309v1
• Date: Sun, 08 Feb 2026 16:42:44 GMT
• Title: Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
• Abstract summary: The framework comprises six integrated components: semantic metadata enhancement, dynamic graph construction, VARK-based profiling, mental state estimation, graph-enhanced retrieval with LLM-powered ranking, and adaptive interface design with iterative learning.<n>This work establishes groundwork for cognitively-aware recommendation systems capable of overcoming cold start limitations through semantic comprehension and psychological modeling, offering personalized, explainable recommendations from initial user contact.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models (LLMs) for content semantic analysis and knowledge graph development, integrated with cognitive profiling based on VARK (Visual, Auditory, Reading/Writing, Kinesthetic) learning preferences. The proposed system tackles multiple cold start dimensions: enriching inadequate item descriptions through LLM processing, generating user profiles from minimal data, and dynamically adjusting presentation formats based on cognitive assessment. The framework comprises six integrated components: semantic metadata enhancement, dynamic graph construction, VARK-based profiling, mental state estimation, graph-enhanced retrieval with LLM-powered ranking, and adaptive interface design with iterative learning. Experimental validation on MovieLens-1M dataset demonstrates the system's capacity for personalized recommendation generation despite limited initial information. This work establishes groundwork for cognitively-aware recommendation systems capable of overcoming cold start limitations through semantic comprehension and psychological modeling, offering personalized, explainable recommendations from initial user contact.

Related papers

• Sparse Personalized Text Generation with Multi-Trajectory Reasoning [95.19317487799292]
  PAT (Personalization with Aligned Trajectories) is a reasoning framework for cold-start LLM personalization.<n>It first retrieves information along two complementary trajectories: writing-style cues from stylistically similar users and topic-specific context from preference-aligned users.<n>It then employs a reinforcement learning-based, iterative dual-reasoning mechanism that enables the LLM to jointly refine and integrate these signals.
  arXiv  Detail & Related papers  (2026-04-27T20:58:49Z)
• A Survey on Generative Recommendation: Data, Model, and Tasks [55.36322811257545]
  generative recommendation reconceptualizes recommendation as a generation task rather than discriminative scoring.<n>This survey provides a comprehensive examination through a unified tripartite framework spanning data, model, and task dimensions.<n>We identify five key advantages: world knowledge integration, natural language understanding, reasoning capabilities, scaling laws, and creative generation.
  arXiv  Detail & Related papers  (2025-10-31T04:02:58Z)
• A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback [5.190121417265426]
  This paper presents an end-to-end personalized learning agent, which integrates a Neural Cognitive Diagnosis model (NCD), a Bounded-Ability Computerized Adaptive Testing strategy (BECAT), and large language models (LLMs)<n> Experiments on the ASSISTments dataset show that the NCD module achieves strong performance on response prediction while yielding interpretable mastery assessments.<n>Overall, the results indicate that the proposed design is effective and practically deployable.
  arXiv  Detail & Related papers  (2025-10-26T07:32:31Z)
• Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach [0.0]
  This study proposes a framework that combines text embeddings with Graph Neural Networks (GNNs) for effective persona classification.<n>Given the absence of dedicated persona classification datasets, we create a manually annotated dataset to facilitate model training and evaluation.<n>Our approach, in particular the integration of GNNs, significantly improves classification performance, especially with limited data.
  arXiv  Detail & Related papers  (2024-12-17T19:27:24Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.40798352740857]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• CELA: Cost-Efficient Language Model Alignment for CTR Prediction [70.65910069412944]
  Click-Through Rate (CTR) prediction holds a paramount position in recommender systems.<n>Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs)<n>We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
  arXiv  Detail & Related papers  (2024-05-17T07:43:25Z)
• Representation Learning with Large Language Models for Recommendation [33.040389989173825]
  We propose a model-agnostic framework RLMRec to enhance recommenders with large language models (LLMs)empowered representation learning.<n>RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals.
  arXiv  Detail & Related papers  (2023-10-24T15:51:13Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments [21.12546768556595]
  VLE is a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models. Experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders.
  arXiv  Detail & Related papers  (2022-06-22T15:53:24Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.