Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

• URL: http://arxiv.org/abs/2307.04996v1
• Date: Tue, 11 Jul 2023 03:24:54 GMT
• Title: Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
• Authors: Ghanshyam Verma, Shovon Sengupta, Simon Simanta, Huan Chen, Janos A. Perge, Devishree Pillai, John P. McCrae, Paul Buitelaar
• Abstract summary: We present two knowledge graph-based approaches for personalized article recommendations for a set of customers. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data.
• Score: 3.6587485160470226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.

Related papers

• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
  G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning. G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
  arXiv  Detail & Related papers  (2025-03-09T03:56:22Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted. Recent research has focused on graph foundation models (GFMs) GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models [19.28217321004791]
  Large Language Models (LLMs) offer a promising way to improve the quality and relevance of Knowledge Graphs for recommendation tasks. We propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task. The framework includes: (1) an LLM-based subgraph augmenter for enriching KGs with high-quality information, (2) a confidence-aware message propagation mechanism to filter noisy triplets, and (3) a dual-view contrastive learning method to integrate user-item interactions and KG data.
  arXiv  Detail & Related papers  (2025-02-06T02:06:48Z)
• Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation [55.5687800992432]
  We propose a plug-and-play framework that synergizes Large Language Models (LLMs) and Knowledge Graphs (KGs) to unveil user preferences. This enables the LLM to transform KG entities into concise natural language descriptions, allowing them to comprehend domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-11-16T11:47:21Z)
• LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning [40.53821858897774]
  We introduce a novel recommender that synergies Large Language Models (LLMs) and Knowledge Graphs (KGs) to enhance the recommendation and provide interpretable results. Our approach significantly enhances both the effectiveness and interpretability of recommender systems.
  arXiv  Detail & Related papers  (2024-06-22T14:14:03Z)
• Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems [55.24980128638365]
  We show the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. We propose a novel ChatCRS framework to decompose the complex CRS task into several sub-tasks. Experimental results on two multi-goal CRS datasets reveal that ChatCRS sets new state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2024-05-03T05:42:57Z)
• A Comprehensive Survey on Self-Supervised Learning for Recommendation [19.916057705072177]
  We provide a review of self-supervised learning frameworks designed for recommender systems, encompassing a thorough analysis of over 170 papers. We elaborate on different self-supervised learning paradigms, namely contrastive learning, generative learning, and adversarial learning, so as to present technical details of how SSL enhances recommender systems in various contexts.
  arXiv  Detail & Related papers  (2024-04-04T10:45:23Z)
• TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance [9.017820815622828]
  A promising approach involves leveraging external knowledge to enrich behavioral-based recommender systems with abundant semantic information. This approach faces two primary challenges: denoising raw external knowledge and adapting semantic representations. We propose an External Knowledge-Enhanced Recommendation method with LLM Assistance.
  arXiv  Detail & Related papers  (2024-03-11T12:04:20Z)
• Finding Paths for Explainable MOOC Recommendation: A Learner Perspective [2.4775868218890484]
  We propose an explainable recommendation system for Massive Open Online Courses (MOOCs) that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets.
  arXiv  Detail & Related papers  (2023-12-11T15:27:22Z)
• On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation [49.18304766331156]
  We propose a new contrastive learning framework for KG-enhanced recommendation. We construct two separate contrastive views for KG and IG, and maximize their mutual information. Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method.
  arXiv  Detail & Related papers  (2023-09-23T14:05:55Z)
• 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)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)
• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems [0.0]
  The bias amplification issue needs to be investigated while using these algorithms. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms.
  arXiv  Detail & Related papers  (2023-01-18T16:29:05Z)
• 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)
• Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Recommendation [36.95691423601792]
  We propose attentive Knowledge-aware convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR) CG-KGR is a novel knowledge-aware recommendation model that enables ample and coherent learning of KGs and user-item interactions. We conduct extensive experiments on four real-world datasets over two recommendation tasks.
  arXiv  Detail & Related papers  (2021-09-05T11:55:20Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)
• Deep Learning on Knowledge Graph for Recommender System: A Survey [36.41255991011155]
  A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG.
  arXiv  Detail & Related papers  (2020-03-25T22:53:14Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

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.