TimeWeaver: Time-Aware Sequential Recommender System via Dual-Stream Temporal Network Article Swipe
Yang Liu
,
Tao Wang
,
Yan Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/systems13100857
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/systems13100857
Recommender systems are data-driven tools designed to assist or automate users’ decision-making. With the growing demand of personalized sequential recommendations in business intelligence or e-commerce, effectively capturing temporal information from massive user-sequence data has become a crucial challenge. State-of-the-art attention-based models often struggle to balance performance with computational cost, while traditional convolutional neural networks suffer from limited receptive fields and rigid architectures that inadequately model dynamic user interests. To address these limitations, this paper proposes TimeWeaver, a time-aware dual-stream network for sequential recommendation, whose core innovations comprise three key components. First, it employs a re-parameterized large-kernel convolution to expand the effective receptive field. Second, we design a Time-Aware Augmentation mechanism that integrates inter-event time-interval information into positional encodings of items. This allows it to perceive the temporal dynamics of user behavior. Finally, we propose a dual-stream architecture to jointly capture dependencies across different time scales. The context stream employs a modern Temporal Convolutional Network (TCN) structure to strengthen the memorization of users’ medium- and long-term interests. In parallel, the dynamic stream leverages an Exponential Moving Average (EMA) mechanism to weight recent behaviors for sensitively capturing users’ immediate interests. This dual-stream design allows TimeWeaver to comprehensively extract both long- and short-term sequential features. Extensive experiments on three public e-commerce datasets demonstrate TimeWeaver’s superiority. Compared to the strongest baseline model, TimeWeaver achieves average relative improvements of 4.62%, 9.59%, and 4.59% across all metrics on the Beauty, Sports, and Toys datasets, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/systems13100857
- https://www.mdpi.com/2079-8954/13/10/857/pdf?version=1759134640
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- 27
- OpenAlex ID
- https://openalex.org/W4414623981
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414623981Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/systems13100857Digital Object Identifier
- Title
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TimeWeaver: Time-Aware Sequential Recommender System via Dual-Stream Temporal NetworkWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-09-29Full publication date if available
- Authors
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Yang Liu, Tao Wang, Yan MaList of authors in order
- Landing page
-
https://doi.org/10.3390/systems13100857Publisher landing page
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https://www.mdpi.com/2079-8954/13/10/857/pdf?version=1759134640Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-8954/13/10/857/pdf?version=1759134640Direct OA link when available
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
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| abstract_inverted_index.information | 28, 114 |
| abstract_inverted_index.innovations | 85 |
| abstract_inverted_index.inter-event | 112 |
| abstract_inverted_index.performance | 45 |
| abstract_inverted_index.sensitively | 183 |
| abstract_inverted_index.traditional | 50 |
| abstract_inverted_index.Augmentation | 108 |
| abstract_inverted_index.architecture | 136 |
| abstract_inverted_index.dependencies | 140 |
| abstract_inverted_index.improvements | 222 |
| abstract_inverted_index.inadequately | 63 |
| abstract_inverted_index.intelligence | 22 |
| abstract_inverted_index.large-kernel | 95 |
| abstract_inverted_index.limitations, | 71 |
| abstract_inverted_index.memorization | 159 |
| abstract_inverted_index.personalized | 17 |
| abstract_inverted_index.superiority. | 211 |
| abstract_inverted_index.Convolutional | 152 |
| abstract_inverted_index.architectures | 61 |
| abstract_inverted_index.computational | 47 |
| abstract_inverted_index.convolutional | 51 |
| abstract_inverted_index.respectively. | 238 |
| abstract_inverted_index.time-interval | 113 |
| abstract_inverted_index.user-sequence | 31 |
| abstract_inverted_index.TimeWeaver’s | 210 |
| abstract_inverted_index.attention-based | 39 |
| abstract_inverted_index.comprehensively | 194 |
| abstract_inverted_index.recommendation, | 82 |
| abstract_inverted_index.recommendations | 19 |
| abstract_inverted_index.State-of-the-art | 38 |
| abstract_inverted_index.decision-making. | 11 |
| abstract_inverted_index.re-parameterized | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.47886928 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |