Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems

• URL: http://arxiv.org/abs/2412.20163v3
• Date: Wed, 12 Feb 2025 16:49:56 GMT
• Title: Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems
• Authors: Minhye Jeon, Seokho Ahn, Young-Duk Seo,
• Abstract summary: We propose a consistent approach that extracts both general and specific topics from both side and context information using large language models (LLMs) Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs.
• Score: 3.0969191504482247
• License:
• Abstract: The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration across various systems remains challenging due to the need for domain expert intervention and differences in system characteristics. To address these issues, we propose a consistent approach that extracts both general and specific topics from both side and context information using LLMs. First, general topics are iteratively extracted and updated from side information. Then, specific topics are extracted using context information. Finally, to address synonymous topics generated during the specific topic extraction process, a refining algorithm processes and resolves these issues effectively. This approach allows general topics to capture broad knowledge across diverse item characteristics, while specific topics emphasize detailed attributes, providing a more comprehensive understanding of the semantic features of items and the preferences of users. Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs.

Related papers

• Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity [0.0]
  This paper presents a novel framework for domain-specific question difficulty estimation, leveraging a suite of NLP techniques and knowledge graph analysis. We introduce four key parameters: Topic Retrieval Cost, Topic Salience, Topic Coherence, and Topic Superficiality. A model trained on these features demonstrates the efficacy of our approach in predicting question difficulty.
  arXiv  Detail & Related papers  (2024-08-23T05:40:35Z)
• Personalized Topic Selection Model for Topic-Grounded Dialogue [24.74527189182273]
  Current models tend to predict user-uninteresting and contextually irrelevant topics. We propose a textbfPersonalized topic stextbfElection model for textbfTopic-grounded textbfDialogue, named textbfPETD. Our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-06-04T06:09:49Z)
• Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
  This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
  arXiv  Detail & Related papers  (2024-01-23T11:25:34Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
  This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
  arXiv  Detail & Related papers  (2023-07-03T16:01:45Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)
• Aspect-Oriented Summarization through Query-Focused Extraction [23.62412515574206]
  Real users' needs often fall more closely into aspects, broad topics in a dataset the user is interested in rather than specific queries. We benchmark extractive query-focused training schemes, and propose a contrastive augmentation approach to train the model. We evaluate on two aspect-oriented datasets and find this approach yields focused summaries, better than those from a generic summarization system.
  arXiv  Detail & Related papers  (2021-10-15T18:06:21Z)
• Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge [77.77684299758494]
  We extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG) First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. To handle the diversity and complexity of KG information, we propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via
  arXiv  Detail & Related papers  (2021-04-12T05:52:20Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)

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.