Multi-Tiered Cascading Negative Sampling For Graph Based Recommender System Article Swipe
Renhao Zhang
,
Guopeng He
,
Tieqiao Chen
,
Bingliang Hu
,
Xinyin Jia
,
Siyuan Li
,
Jia Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4849501/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4849501/v1
Effective negative sampling strategies can accelerate model convergence, suppress excessive randomness in negative sample generation, and enhance the predictive performance of the recommender systems modeled by implicit feedback. However, existing negative sampling methods face two potential issues: limited utilization of user-item collaborative information and treating user-negative sample interactions as driven by a singular motive, thereby ignoring the user’s multi-tiered, gradually progressive implicit preferences, which leads to low-quality negative sampling. To design negative sampling methods suited to different geometric spaces based on their data characteristics, this paper introduces a novel multi-tiered cascading negative sampling method (Multi-Tiered Cascading Negative Sampling, MTCNS) for Euclidean space graph-based collaborative filtering recommender systems. Specifically, this method processes through two cascading levels, producing high-quality overall negative sample embeddings and negative sample feature representations that embody multi-layered progressive relevance semantics. Furthermore, outside the negative sampling method, a multi-task learning framework constructs a contrastive learning auxiliary task to enhance the main task’s performance. Experiments conducted on three real datasets demonstrate that this method improves metrics such as NDCG@20 and Recall@20 by an average of over 1.5%.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4849501/v1
- https://www.researchsquare.com/article/rs-4849501/latest.pdf
- OA Status
- gold
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402055614
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402055614Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4849501/v1Digital Object Identifier
- Title
-
Multi-Tiered Cascading Negative Sampling For Graph Based Recommender SystemWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-30Full publication date if available
- Authors
-
Renhao Zhang, Guopeng He, Tieqiao Chen, Bingliang Hu, Xinyin Jia, Siyuan Li, Jia LiuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4849501/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4849501/latest.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.researchsquare.com/article/rs-4849501/latest.pdfDirect OA link when available
- Concepts
-
Recommender system, Computer science, Graph, Distributed computing, Sampling (signal processing), Theoretical computer science, Machine learning, Telecommunications, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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