Unified Collaborative Filtering over Graph Embeddings

Wang, Pengfei; Chen, Hanxiong; Zhu, Yun; Shen, Huawei; Zhang, Yongfeng

doi:10.1145/3331184.3331224

Cited by 25 publications

(10 citation statements)

References 36 publications

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“…During recent years, Knowledge Graph Embedding (KGE) has prevailed in the field of huge structured knowledge interlinking [22], and its effectiveness has been shown in many different scenarios such as search engine [23][24][25], recommendation system [26][27][28][29][30][31][32], question answering [33][34][35], video understanding [36], conversational AI [37,38] and explainable AI [25,31,39].…”

Section: Related Workmentioning

confidence: 99%

Efficient Non-Sampling Knowledge Graph Embedding

et al. 2021

Proceedings of the Web Conference 2021

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Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negative sampling, i.e., the model aims to maximize some similarity of the connected entities in the KG, while minimizing the similarity of the sampled disconnected entities. Negative sampling helps to reduce the time complexity of model learning by only considering a subset of negative instances, which may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such deficiency, we propose a new framework for KG embedding-Efficient Non-Sampling Knowledge Graph Embedding (NS-KGE). The basic idea is to consider all of the negative instances in the KG for model learning, and thus to avoid negative sampling. The framework can be applied to square-loss based knowledge graph embedding models or models whose loss can be converted to a square loss. A natural side-effect of this non-sampling strategy is the increased computational complexity of model learning. To solve the problem, we leverage mathematical derivations to reduce the complexity of non-sampling loss function, which eventually provides us both better efficiency and better accuracy in KG embedding compared with existing models. Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models. CCS CONCEPTS• Computing methodologies → Knowledge representation and reasoning; Learning latent representations; Neural networks.

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Section: Related Workmentioning

confidence: 99%

Efficient Non-Sampling Knowledge Graph Embedding

et al. 2021

Proceedings of the Web Conference 2021

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“…Inspired by the recent developments of graph neural networks, NGCF [30] and STAR-GCN [46] proposed to perform embedding propagation in the user-item integration graph. In addition, graph embedding technique has been leveraged to unify collaborative filtering with attention mechanism for pairwise user-item relation fusion [29]. However, these models cannot well take the social relational information into consideration.…”

Section: A Deep Collaborative Filtering Techniquesmentioning

confidence: 99%

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

Huang²,

et al. 2020

2020 IEEE International Conference on Data Mining (ICDM)

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Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts have been devoted to developing various neural networkbased social recommender systems, such as attention mechanisms and graph-based message passing frameworks. However, two important challenges have not been well addressed yet: (i) Most of existing social recommendation models fail to fully explore the multi-type user-item interactive behavior as well as the underlying cross-relational inter-dependencies. (ii) While the learned social state vector is able to model pair-wise user dependencies, it still has limited representation capacity in capturing the global social context across users. To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we first design a relation-aware reconstructed graph neural network to inject the cross-type collaborative semantics into the recommendation framework. In addition, we further augment SR-HGNN with a social relation encoder based on the mutual information learning paradigm between low-level user embeddings and high-level global representation, which endows SR-HGNN with the capability of capturing the global social contextual signals. Empirical results on three public benchmarks demonstrate that SR-HGNN significantly outperforms state-ofthe-art recommendation methods. Source codes are available at: https://github.com/xhcdream/SR-HGNN.

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“…The existing methods of video recommendation can be divided into three categories: collaborative filtering methods [9,31,32], contentbased methods [3,5,6,33,37] and hybrid approaches [1,41]. Collaborative filtering (CF) is widely used in recommender systems, which models user interests by exploring user-item interactions with the assumption that people with similar interests tend to make similar choices.…”

Section: Video Recommendationmentioning

confidence: 99%

What Aspect Do You Like

Jiang¹,

Wang

Wei³

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Online micro-video recommender systems aim to address the information explosion of micro-videos and make the personalized recommendation for users. However, the existing methods still have some limitations in learning representative user interests, since the multi-scale time effects, user interest group modeling, and false positive interactions are not taken into consideration. In view of this, we propose an end-to-end Multi-scale Time-aware user Interest modeling Network (MTIN). In particular, we first present an interest group routing algorithm to generate fine-grained user interest groups based on user's interaction sequence. Afterwards, to explore multi-scale time effects on user interests, we design a time-aware mask network and distill multiple temporal information by several parallel temporal masks. And then an interest mask network is introduced to aggregate fine-grained interest groups and generate the final user interest representation. At last, in the prediction unit, the user representation and micro-video candidates are fed into a deep neural network (DNN) for predictions. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, and the experimental results demonstrate that our proposed model achieves substantial gains over the state-of-the-art methods.

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Unified Collaborative Filtering over Graph Embeddings

Cited by 25 publications

References 36 publications

Efficient Non-Sampling Knowledge Graph Embedding

Efficient Non-Sampling Knowledge Graph Embedding

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

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