Collaborative Deep Learning for Recommender Systems

• CF는 sparse할 때 약하다. CTR(Collaborative topic regression)이 등장.
  • CF cannot be used for new products
• hierarchical Bayesian model인 CDL(Collaborative Deep Learning)을 제안한다.
  • deep representation learning은 dl로,
  • rating은 CF로.

• CF의 단점때문에 보조정보(auxiliary information)을 이용하는 method 많이들씀
  1. loosely coupled
     • 보조정보 처리 후 CF에 넣음
  2. tightly coupled
     • the rating information can guide the learning of features
     • Collaborative topic regression (CTR) is a recently proposed tightly coupled method. It is a probabilistic graphical model that seamlessly integrates a topic model, latent Dirichlet allocation (LDA) , and a model-based CF method, probabilistic matrix factorization (PMF) .

C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD, pages 448–456, 2011.

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. JMLR, 3:993–1022, 2003.

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257–1264, 2007.