Graph-embedding-inspired article recommendation model

Article Computer Science, Artificial Intelligence

Cross-domain recommendation based on latent factor alignment

Xu Yu et al.

Summary: The CDCFLFA model proposed in this paper aligns latent factors between domains based on pattern matching, transfers user preferences from the auxiliary domain to the target domain, and achieves more accurate knowledge transfer and recommendation results.

NEURAL COMPUTING & APPLICATIONS (2022)

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Article Computer Science, Information Systems

A deep recommendation model of cross-grained sentiments of user reviews and ratings

Yao Cai et al.

Summary: The study proposes a deep learning recommendation model (DeepCGSR) that integrates textual review sentiments and the rating matrix to extract sentiment feature vectors for users and items. The model consistently outperforms other recommendation models and demonstrates improved evaluation results across various metrics. DeepCGSR has potential in addressing overfitting and cold-start problems and represents a significant advancement in recommendation algorithms design and development.

INFORMATION PROCESSING & MANAGEMENT (2022)

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Article Computer Science, Artificial Intelligence

Representation learning with collaborative autoencoder for personalized recommendation

Yi Zhu et al.

Summary: The CAPR method uses collaborative autoencoder for personalized recommendation, learning feature representations of users and items to address different characteristics and sparsity issues. Experimental results demonstrate the effectiveness of this method compared to others.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Computer Science, Information Systems

Neural and Attentional Factorization Machine-Based Web API Recommendation for Mashup Development

Guosheng Kang et al.

Summary: This paper proposes a hybrid factorization machine model named NAFM, which integrates a deep neural network and an attention mechanism to capture the non-linear and complex feature interactions, outperforming existing state-of-the-art models for service recommendation in comprehensive experiments conducted on a real-world dataset from ProgrammableWeb.

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT (2021)

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Article Automation & Control Systems

A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Zhiwei Guo et al.

Summary: This article proposes a social recommendation framework based on deep graph neural networks for future IoT recommendation systems. It encodes user and item feature spaces and completes missing values in user-item rating matrices through matrix factorization. Experiments confirm the efficiency and stability of the proposed framework.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

Mutual information-based recommender system using autoencoder

Zahra Noshad et al.

Summary: This paper introduces a method that combines user similarity-based and model-based approaches in collaborative filtering, addressing reliability and online updating issues. By using predicted user rating vectors from an autoencoder's output and mutual information to find similar users, a similarity graph is constructed to show significant advantages over other methods on the Netflix movie recommendation dataset.

APPLIED SOFT COMPUTING (2021)

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Article Computer Science, Hardware & Architecture

Recommendation algorithm of probabilistic matrix factorization based on directed trust

Shangshang Xu et al.

Summary: This paper proposes a hybrid method based on probabilistic matrix factorization and directed trust to improve the performance of recommender systems, addressing the sparsity of trust matrix and capturing trust relations among users. Experimental results demonstrate that the proposed algorithm outperforms existing benchmark algorithms.

COMPUTERS & ELECTRICAL ENGINEERING (2021)

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Article Computer Science, Artificial Intelligence