Improving Sequential Recommendation Consistency with Self-Supervised Imitation Article Swipe
Yuan Xu
,
Hongshen Chen
,
Yonghao Song
,
Xiaofang Zhao
,
Zhuoye Ding
,
Zhen He
,
Bo Long
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.14031
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.14031
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
Imitation
Recommender system
Consistency model
Artificial intelligence
Perspective (graphical)
Machine learning
Representation (politics)
Persona
Data consistency
Human–computer interaction
Politics
Operating system
Social psychology
Law
Psychology
Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.14031
- https://arxiv.org/pdf/2106.14031
- OA Status
- green
- Cited By
- 1
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3174032909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3174032909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2106.14031Digital Object Identifier
- Title
-
Improving Sequential Recommendation Consistency with Self-Supervised ImitationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-26Full publication date if available
- Authors
-
Yuan Xu, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding, Zhen He, Bo LongList of authors in order
- Landing page
-
https://arxiv.org/abs/2106.14031Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2106.14031Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2106.14031Direct OA link when available
- Concepts
-
Consistency (knowledge bases), Computer science, Imitation, Recommender system, Consistency model, Artificial intelligence, Perspective (graphical), Machine learning, Representation (politics), Persona, Data consistency, Human–computer interaction, Politics, Operating system, Social psychology, Law, Psychology, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.where | 68 |
| abstract_inverted_index.Though | 15 |
| abstract_inverted_index.global | 94, 96, 107 |
| abstract_inverted_index.model, | 46 |
| abstract_inverted_index.models | 3 |
| abstract_inverted_index.mutual | 104 |
| abstract_inverted_index.paper, | 42 |
| abstract_inverted_index.sparse | 25 |
| abstract_inverted_index.tasks, | 67 |
| abstract_inverted_index.aspects | 121 |
| abstract_inverted_index.benefit | 166 |
| abstract_inverted_index.capture | 4, 74 |
| abstract_inverted_index.extract | 58 |
| abstract_inverted_index.improve | 49 |
| abstract_inverted_index.persona | 72, 84 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.provide | 89 |
| abstract_inverted_index.result, | 30 |
| abstract_inverted_index.session | 97 |
| abstract_inverted_index.student | 142, 168 |
| abstract_inverted_index.Finally, | 112 |
| abstract_inverted_index.datasets | 174 |
| abstract_inverted_index.dynamics | 76 |
| abstract_inverted_index.features | 6 |
| abstract_inverted_index.flexible | 160 |
| abstract_inverted_index.hindered | 22 |
| abstract_inverted_index.history. | 14 |
| abstract_inverted_index.learning | 26, 130 |
| abstract_inverted_index.methods. | 184 |
| abstract_inverted_index.signals. | 27 |
| abstract_inverted_index.temporal | 69 |
| abstract_inverted_index.addition, | 158 |
| abstract_inverted_index.advantage | 116 |
| abstract_inverted_index.establish | 126 |
| abstract_inverted_index.framework | 163 |
| abstract_inverted_index.imitating | 145 |
| abstract_inverted_index.imitation | 129, 162 |
| abstract_inverted_index.knowledge | 61, 134 |
| abstract_inverted_index.user-item | 12 |
| abstract_inverted_index.utilizing | 63 |
| abstract_inverted_index.Imitation. | 55 |
| abstract_inverted_index.Precisely, | 56 |
| abstract_inverted_index.effective, | 16 |
| abstract_inverted_index.framework. | 131 |
| abstract_inverted_index.integrated | 128 |
| abstract_inverted_index.introduced | 100 |
| abstract_inverted_index.knowledge, | 124 |
| abstract_inverted_index.maximizing | 102 |
| abstract_inverted_index.prediction | 148 |
| abstract_inverted_index.real-world | 173 |
| abstract_inverted_index.sequences. | 111 |
| abstract_inverted_index.sequential | 1, 32, 50, 182 |
| abstract_inverted_index.Experiments | 170 |
| abstract_inverted_index.consecutive | 8 |
| abstract_inverted_index.consistency | 52, 60, 70, 73, 98, 133 |
| abstract_inverted_index.effectively | 136, 178 |
| abstract_inverted_index.independent | 120 |
| abstract_inverted_index.information | 105 |
| abstract_inverted_index.interaction | 13, 110 |
| abstract_inverted_index.outperforms | 179 |
| abstract_inverted_index.recommender | 33 |
| abstract_inverted_index.transferred | 139 |
| abstract_inverted_index.Furthermore, | 87 |
| abstract_inverted_index.conventional | 147 |
| abstract_inverted_index.inconsistent | 38 |
| abstract_inverted_index.internalized | 137 |
| abstract_inverted_index.perspective, | 95 |
| abstract_inverted_index.pre-training | 66 |
| abstract_inverted_index.predictions. | 39 |
| abstract_inverted_index.chronological | 81 |
| abstract_inverted_index.recommenders. | 169 |
| abstract_inverted_index.respectively. | 86 |
| abstract_inverted_index.expressiveness | 19 |
| abstract_inverted_index.recommendation | 2, 51, 183 |
| abstract_inverted_index.representation | 18 |
| abstract_inverted_index.sensitivities, | 85 |
| abstract_inverted_index.Self-Supervised | 54 |
| abstract_inverted_index.comprehensively | 114 |
| abstract_inverted_index.self-supervised | 65, 161 |
| abstract_inverted_index.representations. | 156 |
| abstract_inverted_index.state-of-the-art | 181 |
| abstract_inverted_index.user-interaction | 75 |
| abstract_inverted_index.consistency-enhanced | 123, 154 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |