Relation-aware Heterogeneous Graph for User Profiling
- URL: http://arxiv.org/abs/2110.07181v1
- Date: Thu, 14 Oct 2021 06:59:30 GMT
- Title: Relation-aware Heterogeneous Graph for User Profiling
- Authors: Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang
- Abstract summary: We propose to leverage the relation-aware heterogeneous graph method for user profiling.
We adopt the query, key, and value mechanism in a transformer fashion for heterogeneous message passing.
We conduct experiments on two real-world e-commerce datasets and observe a significant performance boost of our approach.
- Score: 24.076585294260816
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: User profiling has long been an important problem that investigates user
interests in many real applications. Some recent works regard users and their
interacted objects as entities of a graph and turn the problem into a node
classification task. However, they neglect the difference of distinct
interaction types, e.g. user clicks an item v.s.user purchases an item, and
thus cannot incorporate such information well. To solve these issues, we
propose to leverage the relation-aware heterogeneous graph method for user
profiling, which also allows capturing significant meta relations. We adopt the
query, key, and value mechanism in a transformer fashion for heterogeneous
message passing so that entities can effectively interact with each other. Via
such interactions on different relation types, our model can generate
representations with rich information for the user profile prediction. We
conduct experiments on two real-world e-commerce datasets and observe a
significant performance boost of our approach.
Related papers
- Our Model Achieves Excellent Performance on MovieLens: What Does it Mean? [43.3971105361606]
We conduct a meticulous analysis of the MovieLens dataset.
There are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform.
We discuss the discrepancy between the interaction generation mechanism that is employed by the MovieLens system and that of typical real-world recommendation scenarios.
arXiv Detail & Related papers (2023-07-19T13:44:32Z) - Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns.
We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
arXiv Detail & Related papers (2022-06-06T15:42:54Z) - 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) - Neural Graph Matching based Collaborative Filtering [13.086302251856756]
We identify two different types of attribute interactions, inner and cross interactions.
Existing models do not distinguish these two types of attribute interactions.
We propose a neural Graph Matching based Collaborative Filtering model (GMCF)
Our model outperforms state-of-the-art models.
arXiv Detail & Related papers (2021-05-10T01:51:46Z) - ConsNet: Learning Consistency Graph for Zero-Shot Human-Object
Interaction Detection [101.56529337489417]
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images.
We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs.
Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
arXiv Detail & Related papers (2020-08-14T09:11:18Z) - Disentangled Graph Collaborative Filtering [100.26835145396782]
Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data.
By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations.
DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
arXiv Detail & Related papers (2020-07-03T15:37:25Z) - Joint Item Recommendation and Attribute Inference: An Adaptive Graph
Convolutional Network Approach [61.2786065744784]
In recommender systems, users and items are associated with attributes, and users show preferences to items.
As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values.
We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
arXiv Detail & Related papers (2020-05-25T10:50:01Z) - 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.