Long- and short-term self-attention network for sequential recommendation
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DOI: 10.1016/j.neucom.2020.10.066
Abstract: Abstract With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns ( i .… read more here.
Keywords: network; term; sequential recommendation; self attention ... See more keywords
ARERec: Attentive Local Interaction Model for Sequential Recommendation
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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
Enhanced Attention Framework for Multi-Interest Sequential Recommendation
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DOI: 10.1109/access.2022.3185063
Abstract: Sequential recommendation tasks predict items to be interacted at the next moment according to users’ historical behavior sequences. A large number of studies have shown that accuracy is not the only evaluation metric in the… read more here.
Keywords: sequential recommendation; enhanced attention; recommendation; interest sequential ... See more keywords
Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation
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DOI: 10.1109/access.2022.3201339
Abstract: At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then… read more here.
Keywords: information; spatiotemporal information; sequential recommendation; recommendation ... See more keywords
Parameter-Efficiently Leveraging Session Information in Deep Learning-Based Session-Aware Sequential Recommendation
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DOI: 10.1109/access.2025.3545243
Abstract: In recommender systems, leveraging user interaction history as sequential information has recently led to significant performance improvements. However, in many online services, user interactions are often grouped into sessions that inherently share user preferences, requiring… read more here.
Keywords: deep learning; sequential recommendation; session; recommendation ... See more keywords
Hyperbolic Translation-Based Sequential Recommendation
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DOI: 10.1109/tcss.2024.3409711
Abstract: The goal of sequential recommendation algorithms is to predict personalized sequential behaviors of users (i.e., next-item recommendation). Learning representations of entities (i.e., users and items) from sparse interaction behaviors and capturing the relationships between entities… read more here.
Keywords: hyperbolic translation; based sequential; translation based; sequential recommendation ... See more keywords
Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.
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DOI: 10.1109/tcyb.2021.3077361
Abstract: Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval… read more here.
Keywords: attention; multivariate hawkes; sequential recommendation; recommendation ... See more keywords
Learning From the Future: Light Cone Modeling for Sequential Recommendation.
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DOI: 10.1109/tcyb.2022.3222259
Abstract: Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user's present interests from his/her past-to-present behaviors, i.e. making recommendations with only the unidirectional… read more here.
Keywords: sequential recommendation; modeling sequential; future light; learning future ... See more keywords
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
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DOI: 10.1109/tkde.2021.3050407
Abstract: Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model… read more here.
Keywords: sequential recommendation; recommendation; dictionary learning; dynamic preferences ... See more keywords
Knowledge Graph-Based Behavior Denoising and Preference Learning for Sequential Recommendation
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DOI: 10.1109/tkde.2023.3325666
Abstract: Sequential recommendation seeks to predict users’ next behaviors and recommend related items over time. Existing research has mainly focused on modeling users’ dynamic preferences from their sequential behaviors. However, most of these studies have ignored… read more here.
Keywords: behavior denoising; information; preference learning; knowledge ... See more keywords
TagRec: Temporal-Aware Graph Contrastive Learning With Theoretical Augmentation for Sequential Recommendation
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DOI: 10.1109/tkde.2025.3538706
Abstract: Sequential recommendation systems aim to predict the future behaviors of users based on their historical interactions. Despite the success of neural architectures like Transformer and Graph Neural Networks, these models often struggle with the inherent… read more here.
Keywords: aware graph; sequential recommendation; temporal aware; contrastive learning ... See more keywords