Abstract: Keyword recommendation methods and systems based on a latent Dirichlet allocation (LDA) model. The method comprises: calculating a basic Dirichlet allocation model for training texts; obtaining an incremental seed word, and selecting from the training texts a training text matching the incremental seed word to serve as an incremental training text; calculating an incremental Dirichlet allocation model for the incremental training text; obtaining a probability distribution of complete words to topics and a probability distribution of complete texts to topics; calculating a relevance score between the complete word and any other complete word respectively to obtain a relevance score between every two complete words; and determining, according to an obtained query word and the obtained relevance score between every two complete words, a keyword corresponding to the query word.

Abstract: Keyword recommendation methods and systems based on a latent Dirichlet allocation (LDA) model. The method comprises: calculating a basic Dirichlet allocation model for training texts; obtaining an incremental seed word, and selecting from the training texts a training text matching the incremental seed word to serve as an incremental training text; calculating an incremental Dirichlet allocation model for the incremental training text; obtaining a probability distribution of complete words to topics and a probability distribution of complete texts to topics; calculating a relevance score between the complete word and any other complete word respectively to obtain a relevance score between every two complete words; and determining, according to an obtained query word and the obtained relevance score between every two complete words, a keyword corresponding to the query word.