Graph convolution machine for context-aware recommender system

Wu, Jiancan; He, Xiangnan; Wang, Xiang; Wang, Qifan; Chen, Weijian; Lian, Jianxun; Xie, Xing

doi:10.1007/s11704-021-0261-8

Cited by 43 publications

(9 citation statements)

References 38 publications

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“…Furthermore, in disease spread modeling [58,59], accurate node identification aids in containing outbreaks and implementing intervention strategies effectively. Additionally, within customer segmentation and recommendation systems, precise node detection enables personalized marketing strategies, tailored product recommendations, and effective customer retention initiatives, thereby enriching user experience and engagement [64][65][66]. This emphasis on precise node detection across these applications underscores its role in facilitating informed decision-making, optimization, and targeting in real-world scenarios, highlighting its indispensability in contemporary graph analysis methodologies.…”

Section: -3-discussionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framework for addressing challenges inherent in graph partitioning tasks across diverse domains. At its core, LouvainSplit utilizes a feature pyramid representation approach to extract both basic and summary features from input graphs at multiple granularity levels. This methodology ensures a subtle evaluation of graph data by capturing fundamental graph information alongside intricate structural patterns, thus offering a comprehensive representation of underlying community structures. A key innovation of our approach is the integration of the Louvain algorithm, renowned for its efficacy in community detection. By leveraging pairwise cosine similarities computed from node feature vectors, the Louvain algorithm optimizes modularity iteratively, effectively partitioning graphs into cohesive communities. This iterative process facilitates the identification of significant network structures within complex networks, providing valuable insights into their organizational dynamics. To comprehensively evaluate LouvainSplit's performance, we integrated diverse graph partitioning algorithms, including PyMetis, Genetic Algorithm (GA), DBSCAN, KMeans, OPTICS, and spectral clustering to evaluate the effectiveness of LouvainSplit and using popular evaluation metrics across diverse benchmarks spanning various domains and graphs. The results demonstrate LouvainSplit's superiority in terms of recall score, modularity, and scalability compared to existing methods. Moreover, LouvainSplit maintains competitive average runtime values, ensuring efficient processing even with large-scale datasets.

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Section: -3-discussionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

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“…Single network embedding methods [8,11,13,18,60,74] learn an information preserving embedding of a single-view network for node classification, node clustering and many other related tasks. A spectral based method [3] has been proposed, which uses the top-k eigenvectors to represent the network nodes.…”

Section: Related Work 21 Single Network Embeddingmentioning

confidence: 99%

Deep Partial Multiplex Network Embedding

Wang¹,

Fang²,

Ravula³

et al. 2022

Preprint

Self Cite

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Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Realworld networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data.In this paper, we present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network, enforcing the data consistency across views via common latent subspace learning, and preserving the data topological structure within the same network through graph Laplacian. We further prove the orthogonal invariant property of the learned embeddings and connect our approach with the binary embedding techniques. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several stateof-the-art methods on node classification, link prediction and clustering tasks. CCS CONCEPTS• Information systems → Web searching and information discovery.

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“…A light graph convolution [ 23 ] is proposed on interaction graph to model users’ interests by simplifying transformation, nonlinear activation in graph learning. Wu et al [ 24 ] explored a context-aware graph convolution on the user–item interaction graph to digest the collaborative signals among users, items, and contexts into interaction estimation. These models aim to use heterogeneous collaborative signals hidden in users’ interactions as much as possible to embed users and items for personalized matching.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation

Jian

Zhang

et al. 2022

Sensors

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Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, which meets great difficulty in modeling users’ interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users’ interests. In this work, we explore the semantic correlations between items on modeling users’ interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users’ interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users’ interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.

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Graph convolution machine for context-aware recommender system

Cited by 43 publications

References 38 publications

Efficient Graph Partitioning using LouvainSplit Algorithm

Efficient Graph Partitioning using LouvainSplit Algorithm

Deep Partial Multiplex Network Embedding

Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation

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