Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering

Zhang, Hengrui; McAuley, Julian

doi:10.1137/1.9781611976236.9

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…We review some graph-based recommendation methods as we adopt the graph neural network (GNN) [38,43,46,54] for the game recommendation. Graph-based recommendation methods model useritem interactions as a bipartite graph [2,14,23,32,51,53], with potential extensions to the heterogeneous graph with additional user-user social graph [25,49] and item knowledge graph [39,40]. Graph-based methods mostly adopt GNN for learning nodes (users and items) embeddings.…”

Section: Graph-based Recommendationmentioning

confidence: 99%

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Yang,

Liu,

Wang

et al. 2022

Preprint

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Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can attract users to dwell longer in the platform. This paper investigates the characteristics of user behaviors with respect to the online games on the Steam platform. Based on the observations, we argue that a satisfying recommender system for online games is able to characterize: personalization, game contextualization and social connection. However, simultaneously solving all is rather challenging for game recommendation. Firstly, personalization for game recommendation requires the incorporation of the dwelling time of engaged games, which are ignored in existing methods. Secondly, game contextualization should reflect the complex and high-order properties of those relations. Last but not least, it is problematic to use social connections directly for game recommendations due to the massive noise within social connections. To this end, we propose a Social-aware Contextualized Graph Neural Recommender System (SCGRec), which harnesses three perspectives to improve game recommendation. We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.

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Section: Graph-based Recommendationmentioning

confidence: 99%

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Yang,

Liu,

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…And then, many GCN-based recommendation models have been developed. For example, GC-MC [29] employs one convolution layer to exploit the direct connections between users and items; PinSage [43] combines random walks with multiple graph convolution layers on the item-item graph for Pinterest image recommendation; MEIRec [8] utilizes metapath-guided neighbors to exploit rich structure information for intent recommendation; NGCF [33] exploits high-order proximity by propagating embeddings on the user-item interaction graph; instead of implicitly capturing the high-order connectivity through the propagation embedding, SMOG-CF [45] is proposed to directly capture the high-order connectivity between neighboring nodes at any order. Multi-GCCF [28] explicitly incorporates the user-user and item-item graphs, which is built upon the user-item bipartite graph, in the embedding learning process.…”

Section: Related Workmentioning

confidence: 99%

Interest-aware Message-Passing GCN for Recommendation

Fan

Cheng

Zhu

et al. 2021

Proceedings of the Web Conference 2021

173

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Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem -when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user's embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly. CCS CONCEPTS• Information systems → Recommender systems.

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“…This work focuses on grouping search results based on the user's broad intent specified in the query. There are a few more research [26], [27], [28], [29] on e-commerce domain where product recommendation was learned using Graph representation which were built using user-item engagement data.…”

Section: Previous Workmentioning

confidence: 99%

Grouping Search Results with Product Graphs in E-commerce Platforms

Ranganath¹,

Das²,

Sanjay³

et al. 2021

Preprint

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Showing relevant search results to the user is the primary challenge for any search system. Walmart e-commerce provides an omnichannel search platform to its customers to search from millions of products. This search platform takes a textual query as input and shows relevant items from the catalog. One of the primary challenges is that this queries are complex to understand as it contains multiple intent in many cases. This paper proposes a framework to group search results into multiple ranked lists intending to provide better user intent. The framework is to create a product graph having relations between product entities and utilize it to group search results into a series of stacks where each stack provides a group of items based on a precise intent. As an example, for a query "milk, " the results can be grouped into multiple stacks of "white milk", "low-fat milk", "almond milk", "flavored milk". We measure the impact of our algorithm by evaluating how it improves the user experience both in terms of search quality relevance and user behavioral signals like Add-To-Cart.

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Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering

Cited by 20 publications

References 19 publications

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Interest-aware Message-Passing GCN for Recommendation

Grouping Search Results with Product Graphs in E-commerce Platforms

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