Collaborative Topic Modeling for Recommending Scientific Articles

Collaborative topic modeling for recommending scientific articles
C Wang, DM Blei - Proceedings of the 17th ACM SIGKDD international …, 2011 - dl.acm.org
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abstract

scientific article추천이 목표

Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling.

intro

관련 paper를 찾는 고전적인 방법은 1)인용 2)keyword search 3)각자가 만든 레퍼런스라이브러리를 공유하게 해줌 등이다.

Collaborative filtering based on latent factor models [17, 18, 13, 1, 22] and content analysis based on probabilistic topic modeling [7, 8, 20, 2].

우리는 둘 다 쓴다.

We combine these approaches in a probabilistic model, where making a recommendation for a particular user is akin to computing a conditional expectation of hidden variables. We will show how the algorithm for computing these expectations naturally balances the influence of the content of the articles and the libraries of the other users. An article that has not been seen by many will be recommended based more on its content; an article that has been widely seen will be recommended based more on the other users.

background

recommendation tasks

I users and J items

our task is to recommend articles that are not in her library but are potentially interesting.

Probabilistic Topic Models

The simplest topic model is latent Dirichlet allocation (LDA) [7].(wiki참고. linear discriminant analysis와는 다른 것이다)

references

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