sparse_btm

a cpp implementation of sparse biterm topic model, 10x faster than origin implementation because using sparse-gibbs-sampler.

features:

• being suitable to model for user-click-sequenece(Rcommandation System) or short-text(NLP), because it assume that adjacent N-items belong to a topic;
• using sparse-gibbs-sampler, 10x faster than origin implementation;

arguments:

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Biterm Topic Model (Sparse-Sampler)

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Parameters:

• -input
  path of docs file, lines of file look like "word1 word2 word3 ... \n"
• -output
  dir of model(topic_biterm_sum, topic_word) file
• -num_topics
  number of topics
• -alpha
  symmetric doc-topic prior probability, default is 0.05
• -beta
  symmetric topic-word prior probability, default is 0.01
• -window_size
  window size for biterms, default is 2
• -num_iters
  number of iteration, default is 20
• -save_step
  save model every save_step iteration, default is -1 (no save)

usage:

./sparse_btm -input short_text.txt -output model_out/ -num_topics 100 -window_size 3 -num-iters 20 -save_step 10