latent dirichlet allocation and its application in...
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Latent Dirichlet Allocation and Its Application in Recommder Systems
Weike Pan
Thanks to Ms. Qing Zhang
College of Computer Science and Software Engineering, Shenzhen University
Introduction
Topic modeling
Introduction
Token vs. term
• Note that word-instance = token and term = word.
• For example, in a document “my name is peter and my nationality is usa”, there are two tokens of the term “my”.
Introduction
Notations
Modeling
Graphical model
Modeling
Generation
Modeling
Objective function
Approximate Inference
• Exact inference
• Approximate inference
– Variational method
– Collapsed Gibbs sampling (we adopt this approach in this slides)
• Collapsed Gibbs Sampling
– A Markov chain Monte Carlo (MCMC) algorithm
– Main idea:
• For the current token w
• Calculate the probability that w belongs to each topic
• Sample a topic according to the probability
Algorithm
Algorithm (Collapsed Gibbs Sampling)
Application in Recommender Systems
• In recommender systems (in particular of one-class collaborative filtering), we may take users as documents, and items as terms, and model the users’ behaviors using LDA.
• Notice that the algorithm in previous pages can be used without modification.
• ...
References
• David M. Blei, Andrew Y. Ng and Michael I. Jordan. Latent Dirichlet Allocation. JMLR 2003.
• Thomas L. Griffiths and Mark Steyvers. Finding Scientific Topics. PNAS 2004.
• David M. Blei. Probabilistic Topic Models. CACM 2012.
• Haijun Zhang, Zhoujun Li, Yan Chen, Xiaoming Zhang and Senzhang Wang. Exploit Latent Dirichlet Allocation for One-Class Collaborative Filtering. CIKM 2014.
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