Document　similarity　computation　is　an　exciting　research　topic　in　Information　Retrieval　(IR)　and　it　is　a　key　issue　for　automatic　document　categorization,　clustering　analysis,　fuzzy　query,　and　question　answering.　Topic　model　is　an　emerging　field　in　Natural　Language　Processing　(NLP),　IR,　and　Machine　Learning　(ML).　In　this　paper,　we　apply　a　Latent　Dirichlet　Allocation　(LDA)　topic　model-based　method　to　compute　similarity　between　documents.　By　mapping　a　document　with　term　space　representation　into　a　topic　space,　a　distribution　over　topics　is　derived　for　computing　document　similarity.　An　empirical　study　using　real　data　set　demonstrates　the　efficiency　of　our　method.　©　2015　Taylor　&　Francis　Group,　London.