Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

Chen, Weijian; Gu, Yun; Ren, Zhaochun; He, Xiangnan; Xie, Hongtao; Guo, Tong; Yin, Dawei; Zhang, Yongdong

doi:10.24963/ijcai.2019/293

Cited by 78 publications

(52 citation statements)

References 19 publications

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“…However, recent years have seen a surge in approaches for automatically learning to encode a graph structure into low-dimensional embedding using techniques based on deep learning and nonlinear dimension reduction. Chen et al [38] exploited graph attention networks (GAT) to learn user node representation by spreading information in heterogeneous graphs and then leveraged limited labels of users to build end-to-end semisupervised user profiling predictor. Zhang et al [25] introduced the problem of heterogeneous graph representation learning and proposed a heterogeneous graph neural networks model HetGNN.…”

Section: Graph Representation Learningmentioning

confidence: 99%

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

Guo

Liu

2021

Wireless Communications and Mobile Computing

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Social and information networks such as Facebook, Twitter, and Weibo have become the main social platforms for the public to share and exchange information, where we can easily access friends’ activities and in turn be influenced by them. Consequently, the analysis and modeling of user retweet behavior prediction have an important application value, such as information dissemination, public opinion monitoring, and product recommendation. Most of the existing solutions for user retweeting behavior prediction are usually based on network topology maps of information dissemination or designing various handcrafted rules to extract user-specific and network-specific features. However, these methods are very complex or heavily dependent on the knowledge of domain experts. Inspired by the successful use of neural networks in representation learning, we design a framework, UserRBPM, to explore potential driving factors and predictable signals in user retweet behavior. We use the graph embedding technology to extract the structural attributes of the ego network, consider the drivers of social influence from the spatial and temporal levels, and use graph convolutional networks and the graph attention mechanism to learn its potential social representation and predictive signals. Experimental results show that our proposed UserRBPM framework can significantly improve prediction performance and express social influence better than traditional feature engineering-based approaches.

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Section: Graph Representation Learningmentioning

confidence: 99%

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

Guo

Liu

2021

Wireless Communications and Mobile Computing

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“…Lastly, we also present a Motion Context, that captures the relationship between a speech and the motion under debate. Motivated by (Chen et al, 2019a) we use heterogeneous graphs to model such contextual information.…”

Section: Gpols: Graph Political Stance Analyzer: Context Modeling and Graph Creationmentioning

confidence: 99%

“…It is possible to initialize user features using external information about the politician, however, this is beyond the scope of this work, and forms our future direction. We follow related literature on user profiling(Chen et al, 2019b; in heterogeneous graph attention network settings and set user features as zero embeddings.…”

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confidence: 99%

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

Sawhney

Wadhwa

Agarwal³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society. However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis. We introduce GPolS, a neural model for political speech stance analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.

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“…(3) The property of balancing the goals of exploration and exploitation alleviates the pressure of losing users caused by the full exploitation in existing meta-learning methods. Lastly, to verify its advantage over state-of-the-arts, we conduct extensive experiments and analysis on three benchmark datasets (MovieLens 4 , EachMovie 5 and Netflix 6 ). The experimental results demonstrate its significant advantage over state-of-the-art methods and the knowledge learned by NICF.…”

Section: Introductionmentioning

confidence: 99%

Neural Interactive Collaborative Filtering

Zou

Xia

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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102

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In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we propose to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods.

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Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

Cited by 78 publications

References 19 publications

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

Neural Interactive Collaborative Filtering

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