OpenMetaRec: Open-metapath heterogeneous dual attention network for paper recommendation

Xiao, Xia; Huang, Jiaying; Wang, Haobo; Zhang, Chengde; Chen, Xinzhong

doi:10.1016/j.eswa.2023.120806

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Algorithm 1 shows the optimization of context-aware eALS. Line 3 precomputes the cache matrix X p according to Equation (10), whose computational complexity is O(|I|K 2 ), and lines 4-8 compute the user's hidden feature matrix, whose computational complexity is O(|U|K 2 + |R|K). Line 9 precomputes the cache matrix X q according to Equation (13), whose computational complexity is O(|U|K 2 ).…”

Section: Complexity Analysismentioning

confidence: 99%

“…Collaborative filtering-based approaches assess a user's reading records and predict the user's preferences for unread papers using methods such as nearest neighbor computation, matrix decomposition [5], and deep learning [6]. Graph-based approaches, which often use homomorphic graphs, such as citation networks [7][8][9], or heteromorphic graphs [10,11], like those constructed by entities such as authors-conferencespapers, generate embeddings of the entities. They then create recommendation lists via meta-path methods [12] or graph neural networks [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

Huang,

Liu,

Wang

2024

Electronics

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Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. In this paper, we address these two issues and improve upon them from the perspective of metric learning. The algorithm is modeled as a push–pull loss function. For the positive sample pull-out operation, we introduce a context factor, which accelerates the convergence of the objective function through the multiplication rule to alleviate the data sparsity problem. For the negative sample push operation, we adopt an unbiased global negative sample method and use an intermediate matrix caching method to greatly reduce the computational complexity. Experimental results on two real datasets show that our method outperforms other baseline methods in terms of recommendation accuracy and computational efficiency. Moreover, our metric learning method that introduces context improves by more than 5% over the element-wise alternating least squares method. We demonstrate the potential of metric learning in addressing the problem of implicit feedback recommender systems with positive and negative sample imbalances.

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Section: Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

Huang,

Liu,

Wang

2024

Electronics

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Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

Liu,

Chen,

Lee

et al. 2024

ACM Trans. Intell. Syst. Technol.

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The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proving that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user’s needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model’s accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism.

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Maser: Multi-Order Attention and Semantic-Enhanced Representation Model for Complex Text Recommendation

Lai

Dai

Liao³

et al. 2023

Preprint

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OpenMetaRec: Open-metapath heterogeneous dual attention network for paper recommendation

Cited by 3 publications

References 30 publications

A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

Maser: Multi-Order Attention and Semantic-Enhanced Representation Model for Complex Text Recommendation

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