Improving Sequential Recommendation Consistency with Self-Supervised Imitation Article Swipe
Yuan Xu
,
Hongshen Chen
,
Yonghao Song
,
Xiaofang Zhao
,
Zhuoye Ding
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/457
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/457
Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential recommender is prone to make inconsistent predictions. In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation. Precisely, we extract the consistency knowledge by utilizing three self-supervised pre-training tasks, where temporal consistency and persona consistency capture user-interaction dynamics in terms of the chronological order and persona sensitivities, respectively. Furthermore, to provide the model with a global perspective, global session consistency is introduced by maximizing the mutual information among global and local interaction sequences. Finally, to comprehensively take advantage of all three independent aspects of consistency-enhanced knowledge, we establish an integrated imitation learning framework. The consistency knowledge is effectively internalized and transferred to the student model by imitating the conventional prediction logit as well as the consistency-enhanced item representations. In addition, the flexible self-supervised imitation framework can also benefit other student recommenders. Experiments on four real-world datasets show that SSI effectively outperforms the state-of-the-art sequential recommendation methods.
Related Topics
Concepts
Consistency (knowledge bases)
Computer science
Recommender system
Imitation
Consistency model
Artificial intelligence
Machine learning
Perspective (graphical)
Representation (politics)
Persona
Data consistency
Human–computer interaction
Political science
Operating system
Psychology
Social psychology
Politics
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2021/457
- https://www.ijcai.org/proceedings/2021/0457.pdf
- OA Status
- gold
- Cited By
- 17
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3187465321
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3187465321Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.24963/ijcai.2021/457Digital Object Identifier
- Title
-
Improving Sequential Recommendation Consistency with Self-Supervised ImitationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Yuan Xu, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye DingList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2021/457Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2021/0457.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.ijcai.org/proceedings/2021/0457.pdfDirect OA link when available
- Concepts
-
Consistency (knowledge bases), Computer science, Recommender system, Imitation, Consistency model, Artificial intelligence, Machine learning, Perspective (graphical), Representation (politics), Persona, Data consistency, Human–computer interaction, Political science, Operating system, Psychology, Social psychology, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 8, 2022: 5Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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