DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph

• URL: http://arxiv.org/abs/2306.13837v1
• Date: Sat, 24 Jun 2023 01:54:49 GMT
• Title: DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph
• Authors: Yajing Yang, Zeyu Zeng, Mao Chen, Ruirui Shang
• Abstract summary: We propose DEKGCI, a novel double-sided recommendation model. We use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.

Related papers

• GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation [13.1192216083304]
  We propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. We then construct two types of user modalities: explicit interaction features and extended interest features.
  arXiv  Detail & Related papers  (2024-07-17T06:29:00Z)
• 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)
• 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)
• IA-GCN: Interactive Graph Convolutional Network for Recommendation [13.207235494649343]
  Graph Convolutional Network (GCN) has become a novel state-of-the-art for Collaborative Filtering (CF) based Recommender Systems (RS) We build bilateral interactive guidance between each user-item pair and propose a new model named IA-GCN (short for InterActive GCN) Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-04-08T03:38:09Z)
• Virtual Relational Knowledge Graphs for Recommendation [15.978408290522852]
  We argue that it is not efficient nor effective to use every relation type for item encoding. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning.
  arXiv  Detail & Related papers  (2022-04-03T15:14:20Z)
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation [74.14009444678031]
  We propose Knowledge-aware Conditional Attention Networks (KCAN) to incorporate knowledge graph into a recommender system. We use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations.
  arXiv  Detail & Related papers  (2021-11-03T09:40:43Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• 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)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.