Zzong's Notes

방문

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Collaborative Filtering

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Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

최초 작성 2026-06-14마지막 수정 2026-10-23읽는 데 1분

노트 편집 ↗

Conclusion

LDA4Rec model, which extends the traditional LDA by incorporating parameters for the popularity of items and conformity of users.

Related

Latent Dirichlet Allocation
categorical distribution
collaborative filtering

References

Paper link

함께 보면 좋은 글

Collaborative Filtering for Implicit Feedback Datasets

Alternating Least Squares

Probabilistic Matrix Factorization

matrix factorization 은 다음과 같은 확률 모델로 바뀔 수 있다.

Factorization Meets the Item Embedding - Regularizing Matrix Factorization with Item Co-occurrence

co-factorization model (CoFactor) 제안

On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing

paper link Abstract & Introduction (Summary) PLSI 와 NMF 는 동일한 목적 함수를 최적화하는 것임을 보임 NMF 의 경우 I-divergence objective function (L_1-normalization NMF) 다만, NMF 와 PLSI 는 서로 다른 알고리즘임...

Scalable Recommendation with Poisson Factorization

본 논문은 hierarchical Poisson matrix factorization(HPF) 모델을 제안.

Logistic Matrix Factorization for Implicit Feedback Data

Factorization Machines

combines the advantages of Support Vector Machines (SVM) with factorization models

Latent Dirichlet Allocation

paper link: https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

SimpleX - A Simple and Strong Baseline for Collaborative Filtering

learning process of collaborative filtering 는 크게 3 개의 요소로 나뉘어진다: 1) interaction encoder, 2)loss functions, 3)negative sampling

Variational Autoencoders for Collaborative Filtering

With Mult-VAE, the authors introduce a generative model with multinomial likelihood, propose a different regularization parameter for the learning objective, and use Bayesian...

Conclusion
Related
References