Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

Zou, Ding; Wei, Wei; Mao, Xian-Ling; Wang, Ziyang; Qiu, Minghui; Zhu, Feida; Cao, Xin

doi:10.48550/arxiv.2204.08807

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…For the problems of dispersion of sequence recommendations, correlation between items and skewness of length distribution, two information enhancement operators were designed to extract high-quality graphs to solve the above problems [21]. For the data sparsity problem existing on the knowledge graph, the performance of the model was effectively improved by treating multilevel graphs as entity and relationship graphs [22]. Even though the advantages of self-supervised learning are huge, it is less applied to multiple interactions of fine-grained users.…”

Section: Self-supervised Representation Learningmentioning

confidence: 99%

A Two-Path Multibehavior Model of User Interaction

Qu,

Wang,

2023

Electronics

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Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.

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Section: Self-supervised Representation Learningmentioning

confidence: 99%

A Two-Path Multibehavior Model of User Interaction

Qu,

Wang,

2023

Electronics

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“…Recently, contrastive learning has renewed a surge of interest [36][37][38] in zero-shot learning, including tasks at both the node and graph levels. Several works [39,40] have noted the ability of contrastive learning to mine supervised signals from the data itself, which can be used to address the problem of sparse supervised signals that exist in zeroshot learning. Recent works have successfully applied contrastive learning to zero-shot learning tasks [41][42][43][44].…”

Section: Contrastive Learning For Zslmentioning

confidence: 99%

CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification

Liu

et al. 2022

Sensors

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Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. However, the Laplacian over-smoothing problem will be severe as the number of GCN layers deepens, which leads the features between nodes toward a tendency to be similar and degrade the performance of zero-shot image classification tasks. We consider two parts to mitigate the Laplacian over-smoothing problem, namely reducing the invalid node aggregation and improving the discriminability among nodes in the deep graph network. We propose a top-k graph pooling method based on the self-attention mechanism to control specific node aggregation, and we introduce a dual structural symmetric knowledge graph additionally to enhance the representation of nodes in the latent space. Finally, we apply these new concepts to the recently widely used contrastive learning framework and propose a novel Contrastive Graph U-Net with two Attention-based graph pooling (Att-gPool) layers, CGUN-2A, which explicitly alleviates the Laplacian over-smoothing problem. To evaluate the performance of the method on complex real-world scenes, we test it on the large-scale zero-shot image classification dataset. Extensive experiments show the positive effect of allowing nodes to perform specific aggregation, as well as homogeneous graph comparison, in our deep graph network. We show how it significantly boosts zero-shot image classification performance. The Hit@1 accuracy is 17.5% relatively higher than the baseline model on the ImageNet21K dataset.

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“…σ is the ELU non-linear function. After that we need to define positive and negative samples for learning, inspired by other applications of comparative learning [25,26], we define positive and negative samples as shown in Figure 3. The same node of another view for the target node is treated as the positive sample, the other nodes of the same view are treated as the intra-view negative sample, and the nodes of another view except for the positive sample are treated as the inter-view negative sample.…”

Section: Graph Cross-view Contrastive Optimizationmentioning

confidence: 99%

Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation

et al. 2022

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Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for the representation of recipes. However, traditional collaborative filtering or content-based recipe recommendation methods tend to focus more on user–recipe interaction information and ignore higher-order semantic and structural information. Recently, graph neural networks (GNNs)-based recommendation methods provided new ideas for recipe recommendation, but there was a problem of sparsity of supervised signals caused by the long-tailed distribution of heterogeneous graph entities. How to construct high-quality representations of users and recipes becomes a new challenge for personalized recipe recommendation. In this paper, we propose a new method, a multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation. Compared with traditional comparative learning, we design a multi-level view to satisfy the requirement of fine-grained representation of users and recipes, and use multiple knowledge-aware aggregation methods for node fusion to finally make recommendations. Specifically, the local-level includes two views, interaction view and semantic view, which mine collaborative information and semantic information for high-quality representation of nodes. The global-level learns node embedding by capturing higher-order structural information and semantic information through a network structure view. Then, a kind of self-supervised cross-view contrastive learning is invoked to make the information of multiple views collaboratively supervise each other to learn fine-grained node embeddings. Finally, the recipes that satisfy personalized preferences are recommended to users by joint training and model prediction functions. In this study, we conduct experiments on two real recipe datasets, and the experimental results demonstrate the effectiveness and advancement of MKCLN.

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Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

Cited by 4 publications

References 35 publications

A Two-Path Multibehavior Model of User Interaction

A Two-Path Multibehavior Model of User Interaction

CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification

Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation

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