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   Articles with "users items" as a keyword



Extended matrix factorization with entity network construction for recommendation

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recommendations! Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"

DOI: 10.1007/s12652-021-03345-z

Abstract: In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender… read more here.

Keywords: users items; recommendation; network; extended matrix ... See more keywords
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Indirect Interactions Discovering and True Negative Sampling for Multimodal Recommendation

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recommendations! Published in 2025 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2025.3571909

Abstract: Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct… read more here.

Keywords: users items; multimodal recommendation; indirect interactions; true negative ... See more keywords

An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems.

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recommendations! Published in 2023 at "IEEE transactions on cybernetics"

DOI: 10.1109/tcyb.2022.3233819

Abstract: Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in… read more here.

Keywords: network; recommendation; users items; hin ... See more keywords

Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit Information.

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3182942

Abstract: In the current matrix factorization recommendation approaches, the item and the user latent factor vectors are with the same dimension. Thus, the linear dot product is used as the interactive function between the user and… read more here.

Keywords: recommendation; users items; factorization recommendation; matrix factorization ... See more keywords

Feature Matching Machine for Cold-Start Recommendation

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recommendations! Published in 2024 at "IEEE Transactions on Services Computing"

DOI: 10.1109/tsc.2023.3334241

Abstract: In recommendation systems, the cold-start issue is a long-standing problem where no historical interaction records are given for certain users or items. Under this circumstance, recommendations for new users or new items become challenging. To… read more here.

Keywords: matching machine; feature matching; cold start; recommendation ... See more keywords

Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN

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recommendations! Published in 2022 at "Algorithms"

DOI: 10.3390/a15030080

Abstract: Accurately predicting user–item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse… read more here.

Keywords: user item; users items; item; recursive rnn ... See more keywords