DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

• URL: http://arxiv.org/abs/2108.11883v1
• Date: Thu, 26 Aug 2021 16:19:59 GMT
• Title: DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
• Authors: Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu
• Abstract summary: 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.
• Score: 59.160401038969795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold-start issue, which can be alleviated if incorporating Knowledge Graphs (KGs) as side information. However, most existing works neglect the facts that node degrees in KGs are skewed and massive amount of interactions in KGs are recommendation-irrelevant. To address these problems, in this paper, we propose Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance distribution of connected items from KGs and samples suitable items for recommendation following this distribution. 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. The code is available online at https://github.com/YuWang-1024/DSKReG.

Related papers

• Heterogeneous Hypergraph Embedding for Recommendation Systems [45.49449132970778]
  We present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec) KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. It fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms.
  arXiv  Detail & Related papers  (2024-07-04T06:09:11Z)
• Does Knowledge Graph Really Matter for Recommender Systems? [34.657764335319754]
  Knowledge graphs (KGs) have been widely introduced in Recommender systems (RSs) to improve recommendation accuracy. In this study, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed) We propose an evaluation framework KG4RecEval to evaluate how much a KG contributes to the recommendation accuracy of a KG-based RS.
  arXiv  Detail & Related papers  (2024-04-04T02:32:58Z)
• 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)
• Graph Neural Bandits [49.85090929163639]
  We propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs) To refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration.
  arXiv  Detail & Related papers  (2023-08-21T15:57:57Z)
• Knowledge Graph Contrastive Learning for Recommendation [32.918864602360884]
  We design a general Knowledge Graph Contrastive Learning framework to alleviate the information noise for knowledge graph-enhanced recommender systems. Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation. We exploit additional supervision signals from the KG augmentation process to guide a cross-view contrastive learning paradigm.
  arXiv  Detail & Related papers  (2022-05-02T15:24:53Z)
• 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)
• KECRS: Towards Knowledge-Enriched Conversational Recommendation System [50.0292306485452]
  chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. external knowledge graphs (KG) have been introduced into chit-chat-based CRS. We propose the Knowledge-Enriched Conversational Recommendation System (KECRS) Experimental results on a large-scale dataset demonstrate that KECRS outperforms state-of-the-art chit-chat-based CRS.
  arXiv  Detail & Related papers  (2021-05-18T03:52:06Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• 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)
• Reinforced Negative Sampling over Knowledge Graph for Recommendation [106.07209348727564]
  We develop a new negative sampling model, Knowledge Graph Policy Network (kgPolicy), which works as a reinforcement learning agent to explore high-quality negatives. kgPolicy navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender.
  arXiv  Detail & Related papers  (2020-03-12T12:44:30Z)

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.