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## source file The file can be downloaded at: https://drive.google.com/file/d/14OtIC8eiDkzoWCTtaUZHcb7eB-bUmtTT/view?usp=sharing ## background (Please refer to the paper for more details. https://arxiv.org/pdf/1902.00851.pdf) The following is one short paragraph from the paper which can help to understand the format of the datafile. ----- In our recommendation platform, items are shown in cascade on a mobile App one by one. Each time the user initiates a request, 50 items are recommended to him/her. As user scrolls down the list and have seen all 50 items, a new request is triggered. �is process is repeated until the user leaves the App or return the top of the cascade, labelled as ”exit” in Figure 2. We use a metric called ”pageid” to distinguish diferent requests in this interaction, similar to the concept of ”page” to a search engine. As the user and the system interact with each other, the system learns how to respond to the state to obtain an optimized accumulative reward. ----- ## dataformat Each line contains 15 columns. The meaning of each column is as fololows: column 1: The id of returned page for the current request, which ranges from 0 to 11. Note that for each page, we return 50 items to the user. column 2: The hour when the request is launched by the user. column 3-5: The features used to profile the user which includes age-level, gender and the level of purchase power. column 6-14: The item-sepcific features/labels. We concat the values of 50 returned items belonging to a request toghether to form a list and separat them by comma. More specifically, column 6: The concatenated list of **position** for each item in the returned list, which ranges from 0 to 600 (12pages * 50items/page). column 7-9: The concatenated list of **predicted ctr/cvr/price** for each item in the returned list. column 10-12: The concatednated list of **isclcik/iscart/isfav** for each item in the returned list to indicate whether the item is cliked/added to cart/added to whishlist by the user. column 13: The concatednated list of **purchase amount** for each item in the returned list. For example, 0.0 means that the user does not purchase this item. 12.0 means that the user spends 12 Yuan on this item. column 14: The concatednated list of **an optinal powerful feature** of the item which can be used as one dimension of the "state" vector in RL. column 15: To indicate whether the current page is the last page browsed by the user. So column 1-9,14 can be used to generate **state** in the paper. Column 10-13 can be used to calclulate the **reward** in the paper. Column 15 represents the **terminal** indicator of RL in the paper.