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   Articles with "user item" as a keyword



Local low-rank Hawkes processes for modeling temporal user–item interactions

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recommendations! Published in 2019 at "Knowledge and Information Systems"

DOI: 10.1007/s10115-019-01379-6

Abstract: Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is… read more here.

Keywords: local low; low rank; user item; hawkes processes ... See more keywords

Knowledge-Aware Multi-view Contrastive Learning for Recommendation

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recommendations! Published in 2025 at "Neural Processing Letters"

DOI: 10.1007/s11063-025-11750-0

Abstract: Knowledge-aware Recommendation (KGR) aims to utilize a knowledge graph to provide rich side information for items in a recommendation system and construct a unified graph containing users, items, and entities. In this paper, we present… read more here.

Keywords: user item; contrastive learning; recommendation; item ... See more keywords

ARERec: Attentive Local Interaction Model for Sequential Recommendation

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3160466

Abstract: Previous sequential-recommendation methods have been able to capture patterns of item characteristics that interact with the user. However, they modeled user behavior using a whole interaction sequence, despite possible changes in a user’s behavior over… read more here.

Keywords: user item; sequence; sequential recommendation; interaction ... See more keywords

HIGnet: Hierarchical and Interactive Gate Networks for Item Recommendation

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recommendations! Published in 2020 at "IEEE Intelligent Systems"

DOI: 10.1109/mis.2020.3005928

Abstract: Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of… read more here.

Keywords: item; item recommendation; user item; hierarchical interactive ... See more keywords

TIRAGNN: Temporal and Implicit Relation-Aware Graph Neural Networks for Social Recommendation

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

DOI: 10.1109/tcss.2025.3569680

Abstract: Social recommendation systems predict user preferences by using social relationships to address data sparsity and cold-start problems. Since social relations and user–item interactions can naturally be modeled as graph structures, graph neural networks (GNNs) have… read more here.

Keywords: social recommendation; graph neural; user item; recommendation ... See more keywords

Attribute Graph Neural Networks for Strict Cold Start Recommendation

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recommendations! Published in 2022 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2020.3038234

Abstract: Rating prediction is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness,… read more here.

Keywords: strict cold; neural networks; cold start; user item ... See more keywords

Incorporating Price into Recommendation With Graph Convolutional Networks

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recommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2021.3091160

Abstract: In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images,… read more here.

Keywords: recommendation; user item; incorporating price; item ... See more keywords

Graph Cross-Correlated Network for Recommendation

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3491778

Abstract: Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-item interaction graphs, graph-based… read more here.

Keywords: user item; recommendation; cross correlated; graph ... See more keywords

LETTER: Self-Harmonized Representation Learning for Multimodal Recommendation

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recommendations! Published in 2025 at "IEEE Transactions on Multimedia"

DOI: 10.1109/tmm.2025.3623549

Abstract: Multimodal recommender systems try to integrate multimedia data (images, texts, etc.) with user-item historical records to better model user preference. However, most previous methods largely ignored the underlying fine-grained attribute features of items, which makes… read more here.

Keywords: letter; self harmonized; multimodal recommendation; user item ... See more keywords

HNF: Hybrid Neural Filtering Based on Centrality-Aware Random Walk for Personalized Recommendation

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recommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"

DOI: 10.1109/tnse.2021.3100864

Abstract: Social computing which analyzes users’ behaviors can help personalized recommender system to extract preferences of users. Most of personalized recommender systems exploit a user-item rating matrix to learn representations of users and items for predicting… read more here.

Keywords: recommendation; user item; hnf; neural filtering ... 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