Disentangled variational auto-encoder enhanced by counterfactual data for debiasing recommendation Article Swipe
Yupu Guo
,
Fei Cai
,
Jianming Zheng
,
Xin Zhang
,
Honghui Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-023-01314-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-023-01314-x
Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., popularity bias and amplified subjective bias. However, existing debias methods usually concentrate on correcting a single bias. Such single-functionality debiases neglect the bias-coupling issue in which the recommended items are collectively attributed to multiple biases. Besides, previous work cannot tackle the lacking supervised signals brought by sparse data, yet which has become a commonplace in the recommender system. In this work, we introduce a disentangled debias variational auto-encoder framework (DB-VAE) to address the single-functionality issue as well as a counterfactual data enhancement method to mitigate the adverse effect due to the data sparsity. In specific, DB-VAE first extracts two types of extreme items only affected by a single bias based on the collier theory, which are, respectively, employed to learn the latent representation of corresponding biases, thereby realizing the bias decoupling. In this way, the exact unbiased user representation can be learned by these decoupled bias representations. Furthermore, the data generation module employs Pearl’s framework to produce massive counterfactual data to help fully train the model, making up the lacking supervised signals due to the sparse data. Extensive experiments on three real-world data sets demonstrate the effectiveness of our proposed model. Specifically, our model outperforms the best baseline by 19.5% in terms of Recall@20 and 9.5% in terms of NDCG@100 in the best scenario. Besides, the counterfactual data can further improve DB-VAE, especially on the data set with low sparsity.
Related Topics
Concepts
Counterfactual thinking
Recommender system
Computer science
Debiasing
Autoencoder
Machine learning
Artificial intelligence
Encoder
Benchmark (surveying)
Representation (politics)
Data mining
Deep learning
Law
Politics
Geodesy
Philosophy
Political science
Operating system
Geography
Cognitive science
Psychology
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-023-01314-x
- https://link.springer.com/content/pdf/10.1007/s40747-023-01314-x.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390766123
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390766123Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s40747-023-01314-xDigital Object Identifier
- Title
-
Disentangled variational auto-encoder enhanced by counterfactual data for debiasing recommendationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-12Full publication date if available
- Authors
-
Yupu Guo, Fei Cai, Jianming Zheng, Xin Zhang, Honghui ChenList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-023-01314-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40747-023-01314-x.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s40747-023-01314-x.pdfDirect OA link when available
- Concepts
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Counterfactual thinking, Recommender system, Computer science, Debiasing, Autoencoder, Machine learning, Artificial intelligence, Encoder, Benchmark (surveying), Representation (politics), Data mining, Deep learning, Law, Politics, Geodesy, Philosophy, Political science, Operating system, Geography, Cognitive science, Psychology, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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