CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910322999164928 |
|---|---|
| author | Wang, Jun Li, Haoxuan Zhang, Chi Liang, Dongxu Yu, Enyun Ou, Wenwu Wang, Wenjia |
| author_facet | Wang, Jun Li, Haoxuan Zhang, Chi Liang, Dongxu Yu, Enyun Ou, Wenwu Wang, Wenjia |
| contents | Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the performance of recommender systems in terms of accuracy and ranking. Existing solutions for handling the issues, such as data imputation and inverse propensity score, are highly susceptible to additional trained imputation or propensity models. In this work, we propose a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing data by exploiting the advances in contrast learning. Specifically, the proposed CounterCLR employs a deep representation network, called CauNet, to infer non-random missing data in recommendations and perform user preference modeling by further introducing a self-supervised contrastive learning task. Our CounterCLR mitigates the selection bias problem without the need for additional models or estimators, while also enhancing the generalization ability in cases of sparse data. Experiments on real-world datasets demonstrate the effectiveness and superiority of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation Wang, Jun Li, Haoxuan Zhang, Chi Liang, Dongxu Yu, Enyun Ou, Wenwu Wang, Wenjia Information Retrieval Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the performance of recommender systems in terms of accuracy and ranking. Existing solutions for handling the issues, such as data imputation and inverse propensity score, are highly susceptible to additional trained imputation or propensity models. In this work, we propose a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing data by exploiting the advances in contrast learning. Specifically, the proposed CounterCLR employs a deep representation network, called CauNet, to infer non-random missing data in recommendations and perform user preference modeling by further introducing a self-supervised contrastive learning task. Our CounterCLR mitigates the selection bias problem without the need for additional models or estimators, while also enhancing the generalization ability in cases of sparse data. Experiments on real-world datasets demonstrate the effectiveness and superiority of our method. |
| title | CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2402.05740 |