Tensorflow中推荐系统深度学习模型的实现_Python_Jupyter Notebook_下载.zip

This dataset, citeulike-t, was used in the paper 'Collaborative Topic Regression with Social Regularization' [Wang, Chen and Li]. It was collected from CiteULike and Google Scholar. CiteULike allows users to create their own collections of articles. There are abstracts, titles, and tags for each article. Other information like authors, groups, posting time, and keywords is not used in this paper 'Collaborative Topic Regression with Social Regularization' [Wang, Chen and Li]. The details can be found at http://www.citeulike.ort/faq/data.adp. It is collected by us independently from the dataset citeulike-a. We manually select 273 seed tags and collect all the articles with at least one of these tags. We also crawl the citations between the articles from Google Scholar. Note that the final number of tags associated with all the collected articles is far more than the number (273) of seed tags. The text information (item content) of citeulike-a is preprocessed by following the same procedure as that in citeulike-a. After removing the stop words, we choose the top 20000 discriminative words according to the tf-idf values as our vocabulary. Some statistics are listed as follows: #users 7947 #items 25975 #tags 52946 #citations 32565 #user-item pairs 134860 DATA FILES citations.dat citations between articles tag-item.dat articles corresponding to tags, one line corresponds to articles relating to the same tags (note that it is different from the other dataset citeulike-a and that this is the version prior to preprocess thus would have more tags than used in the paper) mult.dat bag of words for each article rawtext.dat raw data tags.dat tags, sorted by tag-id's users.dat rating matrix (user-item matrix) vocabulary.dat corresponding words for file mult.dat BibTex @inproceedings{DBLP:conf/ijcai/WangCL13, author = {Hao Wang and Binyi Chen and Wu-Jun Li}, title = {Collaborative Topic Regression with Social Regularization for Tag Recommendation}, booktitle = {IJCAI}, year = {2013}, ee = {http://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/7006}, crossref = {DBLP:conf/ijcai/2013}, bibsource = {DBLP, http://dblp.uni-trier.de} } @proceedings{DBLP:conf/ijcai/2013, editor = {Francesca Rossi}, title = {IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013}, booktitle = {IJCAI}, publisher = {IJCAI/AAAI}, year = {2013}, isbn = {978-1-57735-633-2}, bibsource = {DBLP, http://dblp.uni-trier.de} }