## 4 Experimental Setup
### Datasets  
We experimented with two different datasets: MovieLens-1M (Movielens-1M dataset: [[https://grouplens.org/datasets/movielens/1m/](https://grouplens.org/datasets/movielens/1m/)]([https://grouplens.org/datasets/movielens/1m/](https://grouplens.org/datasets/movielens/1m/))) and Ad Display/Click Data on Taobao.com (Ad Display/Click Data on Taobao.com dataset: [[https://tianchi.aliyun.com/dataset/dataDetail?dataId=56](https://tianchi.aliyun.com/dataset/dataDetail?dataId=56)]([https://tianchi.aliyun.com/dataset/dataDetail?dataId=56](https://tianchi.aliyun.com/dataset/dataDetail?dataId=56))). We name \"Ad Display/Click Data on Taobao.com\" dataset \"Taobao\" in the rest of the paper for short. The descriptive statistics for the datasets and attributes used as features are shown in Tables 2-4.  
_MovieLens-1M dataset._ The MovieLens-1M dataset contains approximately one million explicit movie ratings (ranging from 1 to 5), users' demographic information and movies' basic information from the MovieLens website. We used both users' and items' attributes as the input features. To make it suitable for CTR prediction task, we followed [38] and transformed ratings into implicit feedback; each entry that was marked with 1 indicated that the user had rated the item positively, and we sampled negative examples from an unwatched set marked as 0 for each user, which had the same numbers as the rated ones. The threshold of positive ratings was set to 4. For the dataset splitting procedure, we split data based on userID; therefore, 3622 (60%), 1207 (20%) and 1207 (20%) users among 6036 users were randomly sampled into the training set (441,134 samples), validation set (149,128 samples) and test set (151,438 samples).  
_Taobao dataset._ Taobao dataset contains over 26 million ad display/click logs from 8 days, users' and items' basic information from Taobao website. We used both users' and items' attributes as the input features. Samples whose \"clk\" field is 1 were treated as positive samples, otherwise as negative samples. Users with low activity, i.e., the ones who have less than 5 positive samples, were filtered from the dataset. Similar to the unwatched set generated for Movielens-1M, an unclicked set was sampled as negative samples for Taobao dataset, which had the same number with the positive ones. In addition to log data, attribute values of the numerical field, i.e., price, were normalized to the range \([0,1]\). Same as the split method for Movielens-1M, we split Taobao dataset based on userID, thus 30,295 (60%), 10,097 (20%) and 10,097 (20%) users among 50,489 users were randomly sampled into training set (443,640 samples), validation set (148,264 samples) and test set (148,534 samples).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **MovieLens-1M** & **Taobao** \\ \hline Number of users & 6036 & 50,489 \\ Number of items & 2347 & 376,764 \\ Number of interactions & 753,772 & 841,416 \\ Median of interactions per user & 78 & 14 \\ Average number of interactions per user & 125 & 17 \\ Number of fields & 6 & 13 \\ Number of features & 3486 & 460,995 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for the datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Type (\# Fields)** & **Field Name** & **Field Type** & **\# Categories** \\ \hline \multirow{3}{*}{User Attributes (4)} & gender & Categorical & 2 \\  & age & Categorical & 7 \\  & occupation & Categorical & 21 \\  & zipcode & Categorical & 3402 \\ \hline \multirow{2}{*}{Item Attributes (2)} & release\_year & Categorical & 36 \\  & genre & Categorical & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attribute statistics for the Movielens-1M dataset.