Each line in the files corresponds to a user visit. An example line is as follows:

1317513291 id-560620 0 |user 1 9 11 13 23 16 18 17 19 15 43 14 39 30 66 50 27 104 20 |id-552077 |id-555224 |id-555528 |id-559744 |id-559855 |id-560290 |id-560518 |id-560620 |id-563115 |id-563582 |id-563643 |id-563787 |id-563846 |id-563938 |id-564335 |id-564418 |id-564604 |id-565364 |id-565479 |id-565515 |id-565533 |id-565561 |id-565589 |id-565648 |id-565747 |id-565822

which contains the following fields delimited with spaces:

* timestamp: e.g., 1317513291
* displayed_article_id: e.g., id-560620
* user_click (0 for no-click and 1 for click): e.g., 0
* string “|user” indicates the start of user features
* user features are 136-dimensional binary vectors; the IDs of nonzero features are listed after the string “|user”
* The pool of available articles for recommendation for each user visit is the set of articles that appear in that line of data. All user IDs (bcookies in our data) are replaced by a common string “user”.

Note that each user is associated with a 136-dimensional binary feature vector. Features IDs take integer values in {1,2,…,136}. Feature #1 is the constant (always 1) feature, and features #2-136 correspond to other user information such as age, gender, and behavior-targeting features, etc. Some user features are not present, since not all users logged in to Yahoo! when they visited the front page.

A unique property of this data set is that the displayed article is chosen uniformly at random from the candidate article pool. Therefore, one can use an unbiased offline evaluation method [1] to compare bandit algorithms in a reliable way.

Related publications for further information:
* Evaluation methodology: http://dx.doi.org/10.1145/1935826.1935878
* Reference performance: http://doi.acm.org/10.1145/1772690.1772758
* First version of the data, collected for a different period of time: http://webscope.sandbox.yahoo.com/catalog.php?datatype=r (“R6″)