Review: [SIGIR'22]Interpolative Distillation for Unifying Biased and Debiased Recommendation
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This document summarizes a paper that proposes a new recommender system model called InterD that aims to perform well on both biased and debiased test sets. InterD uses distillation to train a student model using predictions from pre-trained biased and unbiased teacher models. The student model learns a weighted sum of the teacher predictions based on the environment (biased vs debiased) to determine recommendations. It also incorporates unobserved user-item pairs in training to further improve performance on both test sets. The experiments section evaluates InterD on benchmark datasets to demonstrate its effectiveness at achieving good performance on both biased and debiased recommendations.
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![Motivation
2
▪ Most recommender system’s test set for evaluate
▪ Normal biased test set(𝐷𝑏)
▪ Debiased test set (𝐷𝑑)
[1] Self-supervised Graph Learning for Recommendation, Jiancan Wu(USTC) et al, SIGIR’21
Existing models didn’t perform well on both test set
Biased or Unbiased model
Only reflect part of whole picture](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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- 1. Interpolative Distillation for Unifying Biased and Debiased Recommendation SIGIR’22, Sihao Ding(USTC) et al. POSTECH DI Lab Presenter: Changsoo Kwak 2022.5.24 1
- 2. Motivation 2 ▪ Most recommender system’s test set for evaluate ▪ Normal biased test set(𝐷𝑏) ▪ Debiased test set (𝐷𝑑) [1] Self-supervised Graph Learning for Recommendation, Jiancan Wu(USTC) et al, SIGIR’21 Existing models didn’t perform well on both test set Biased or Unbiased model Only reflect part of whole picture
- 3. Intuitive solution? 3 ▪ Unifying 𝐷𝑏, 𝐷𝑑 ▪ Usually 𝐷𝑏 ≫ |𝐷𝑑| ▪ Train two models for 𝐷𝑏, 𝐷𝑑 respectively, and ensemble ▪ Unclear that each models are strong/weak at which type of users/items ▪ Existing ensemble strategies are not tailored for win-win recommendation scenario ▪ Possible solution? ▪ Distillation! ▪ Aggregate two models at the level of user-item pair Determine coefficient automatically for distillation
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- 5. Proposed model(InterD) 5 𝑓𝑏, 𝑓𝑑: Pre-trained biased/unbiased model ▪ Estimate 𝑃(𝑅|𝑈, 𝐼, 𝐸) ▪ Directly use prediction of 𝑓𝑏, 𝑓𝑑 ▪ Estimate 𝑃 𝐸 𝑈, 𝐼 𝑤𝑏 = 𝐿𝑏(𝑟𝑏, 𝑟)𝛾1 𝐿𝑏(𝑟𝑏, 𝑟)𝛾1+𝐿𝑑(𝑟𝑑, 𝑟)𝛾1 , 𝑤𝑑 = 𝐿𝑑(𝑟𝑑, 𝑟)𝛾1 𝐿𝑏(𝑟𝑏, 𝑟)𝛾1+𝐿𝑑(𝑟𝑑, 𝑟)𝛾1 𝐿𝑏: MSE, 𝐿𝑑: IPS weighted MSE, 𝛾1: Negative hyperparameter 𝑃 𝑅 𝑈, 𝐼 = 𝐸 𝑃 𝑅 𝑈, 𝐼, 𝐸 𝑃 𝐸 𝑈, 𝐼 = 𝑟∗ = 𝑤𝑏 𝑟𝑏 + 𝑤𝑑𝑟𝑑 ▪ Training student model Distillation loss 𝐿𝑂 = 1 |𝐷𝑏| + |𝐷𝑑| (𝑢,𝑖,𝑟)∈𝐷𝑏∪𝐷𝑑 𝐿(𝑟, 𝑟∗ )
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Editor's Notes
- RCT: Randomized Control Trial(https://books.google.co.kr/books?id=JUTqDwAAQBAJ&pg=PA244&lpg=PA244&dq=yahoo!r3+randomized+controlled+trial&source=bl&ots=0cagKMc4KG&sig=ACfU3U3oFb-FZsxO3PuYDFYRz6gX9O97tA&hl=ko&sa=X&ved=2ahUKEwj5qp-psev3AhWim1YBHfVgC2QQ6AF6BAgDEAM#v=onepage&q=yahoo!r3%20randomized%20controlled%20trial&f=false)
- In other words, the student tends to learn the easier aspects of knowledge since the smaller distance makes it easier to follow the corresponding teacher 학생 입장에서 더 쉬운 쪽(거리가 적은 쪽 teacher)을 따라가기 때문에 curriculum learning으로 볼 수도 있다? Weight 계산에 student prediction이 들어가니까 self-paced learning으,로 볼 수도 있다?