P. Gopalan, L. Charlin, D.M. Blei, Content-based recommendations with Poisson factorization, NIPS 2014.
Required libraries: gsl, gslblas, pthread
On Linux/Unix run
./configure make; make install
Note: We have NOT tested the code on Mac. Please use Linux.
On Mac OS, the location of the required gsl, gslblas and pthread libraries may need to be specified:
./configure LDFLAGS="-L/opt/local/lib" CPPFLAGS="-I/opt/local/include" make; make install
The binary 'collabtm' will be installed in /usr/local/bin unless a different prefix is provided to configure. (See INSTALL.)
collabtm [OPTIONS]
-dir <string> path to dataset directory with files described under INPUT below
-mdocs <int> number of documents
-nuser <int> number of users
-nvocab <int> size of vocabulary
-k <int> latent dimensionality
-fixeda fix the document length correction factor ('a') to 1
-lda-init use LDA based initialization (see below)OPTIONAL:
-binary-data treat observed ratings data as binary; if rating > 0 then rating is treated as 1
-doc-only use document data only
-ratings-only use ratings data only
-content-only use both data, but predict only with the topic affinities (i.e., topic offsets are 0)EXPERIMENTAL:
-online use stochastic variational inference
-seq-init -doc-only use sequential initialization for document only fitsWe recommend running CTPF using the following options:
~/src/collabtm -dir
If the document lengths are expected to vary significantly, we recommend additionally running without the "-fixeda" option above.
The above options depend on LDA-based fits being available for the document portion of the model. See below.
The following files must be present in the data directory (as indicated by the '-dir' switch):
train.tsv, test.tsv, validation.tsv, test_users.tsv
train/valid/test files contain triplets in the following format (one per line): userID itemID rating
where tab characters separate the fields.
test_users.tsv contains the userIDs of all users that are tested on (one per line).
The new files additionally needed are mult.dat and vocab.dat. (They are really text files.) This is the "document" portion of the data. Each line of mult.dat is a document and has the following format:
<number of words> <word-id0:count0> <word-id1:count1>....Each line of vocab.dat is a word. Note that both the word index and the document index starts at 0. So a word-id in vocab.dat can be 0 and the document id "rated" in train.tsv can be 0.
Run two versions -- with the correction scalar 'a' inferred and one with 'a' fixed at a 1. One of these fits might be better than the other. The "-fixeda" option specifies that the documents are of similar lengths.
Always use LDA-based initialization.
~/src/collabtm -dir
~/src/collabtm -dir
Run Chong's gibbs sampler to obtain LDA fits on the word frequencies (see below for details)
Create a directory "lda-fits" within the "dataset directory" above and put
two files in it: the topics beta-lda-k
Run collabtm inference with the -lda-init option as follows (the -fixeda option fixes 'a' at 1):
~/src/collabtm -dir
~/src/collabtm -dir
The LDA code is provided under the "lda" directory.
For example, run LDA with parameters
./lda --directory fit_50/ --train_data ~/arxiv/dat/mult_lda.dat --num_topics 50 --eta 0.01 --alpha 0.1 --max_iter -1 --max_time -1
mult_lda.dat contains the documents (see the David Blei's lda-c package for the exact format: http://www.cs.princeton.edu/~blei/lda-c/index.html)
Note The values of eta and alpha need to reflect those used when loading the LDA fits in CTPF (see collabtm.cc:initialize()).
The output directory ("fit_50/" in the above example) will contain the fit files which
can be used to initialize CTPF with -lda-init option. Specifically .topics corresponds
to beta-lda-k