Graph Neural Networks for Social Recommendation

Fan, Wenqi; Ma, Yao; Li, Qing; He, Yuan; Zhao, Eric; Tang, Jiliang; Yin, Dawei

doi:10.1145/3308558.3313488

Cited by 1,365 publications

(780 citation statements)

References 32 publications

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“…These methods aim to represent each object in the user-item interaction network with a low-dimensional continuous embedding vector z ∈ R d , expecting that similar users or items have similar embeddings. Among them, graph neural networks (GNNs) [16,24], as a special instantiation of neural networks for structured data, have achieved state-of-the-art performance in information retrieval [10]. Despite their retrieval quality, the computational costs to filter such a number of candidates in continuous embedding space are expensive, due to the inference inefficiency with O(Nd) computational complexity for linear search in the worse case, where N is the total number of objects in corpus.…”

Section: Introductionmentioning

confidence: 99%

Learning to Hash with Graph Neural Networks for Recommender Systems

Tan

Liu

Zhao

et al. 2020

Proceedings of the Web Conference 2020

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Recommender systems in industry generally include two stages: recall and ranking. Recall refers to efficiently identify hundreds of candidate items that user may interest in from a large volume of item corpus, while the latter aims to output a precise ranking list using complex ranking models. Recently, graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes. Specifically, a deep hashing with GNNs (HashGNN) is presented, which consists of two components, a GNN encoder for learning node representations, and a hash layer for encoding representations to hash codes. The whole architecture is trained end-to-end by jointly optimizing two losses, i.e., reconstruction loss from reconstructing observed links, and ranking loss from preserving the relative ordering of hash codes. A novel discrete optimization strategy based on straight through estimator (STE) with guidance is proposed. The principal idea is to avoid gradient magnification in back-propagation of STE with continuous embedding guidance, in which we begin from learning an easier network that mimic the continuous embedding and let it evolve during the training until it finally goes back to STE. Comprehensive experiments over three publicly available and one real-world Alibaba company datasets demonstrate that our model not only can achieve comparable performance compared with its continuous counterpart but also runs multiple times faster during inference.

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Section: Introductionmentioning

confidence: 99%

Learning to Hash with Graph Neural Networks for Recommender Systems

Tan

Liu

Zhao

et al. 2020

Proceedings of the Web Conference 2020

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“…Table 2 and Fig. 3 present the recommendation performance of all the methods on the whole dataset and on the cold-start user set respectively, and we can make the following observations: 5 The implementation of RSGAN: https://github.com/Coder-Yu/RecQ 1) In all the cases in Table 2, RSGAN outperforms its counterparts on both the relevance and ranking metrics. Specifically, RSGAN achieves good results in terms of the item ranking and the largest relative improvement (calculated by comparing with the second best performance) is more than 11%.…”

Section: B Recommendation Performancementioning

confidence: 88%

“…The instance in this section just sets an example for other applications. Finally, there is an for each user u do 5 Input the seeded friends S u into the generator; 6 Generates p u over the whole user set; 7 Feed p u into the first Gumbel-Softmax layer; 8 Get a one-hot vector representing the friend v; Look-up operation for the items consumed by this friend;…”

Section: E Adversarial Trainingmentioning

confidence: 99%

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Gao

Yin

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.

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“…Finally, the learned user features are integrated into the probabilistic matrix factorization for rating prediction. Base on this, they further proposed a novel attention neural network (GraphRec) (Fan et al 2019) to jointly model the user-item interactions and user social relations. First, the user embedding is learned via aggregating both (1) the user's opinions towards interacted items with different attention scores and (2) the influence of her social friends with different attention scores.…”

Section: Deep Learning Models With Network Features (Dlms+nfs)mentioning

confidence: 99%

Research commentary on recommendations with side information: A survey and research directions

Sun

Guo

Yang

et al. 2019

Electronic Commerce Research and Applications

155

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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.

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Graph Neural Networks for Social Recommendation

Cited by 1,365 publications

References 32 publications

Learning to Hash with Graph Neural Networks for Recommender Systems

Learning to Hash with Graph Neural Networks for Recommender Systems

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Research commentary on recommendations with side information: A survey and research directions

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