Upload Frappe_x1

Frappe_x1.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45f6d52c8d57400e6a2767f6fbd121b35ca9c9e67470ae409425189f3c4ec737
+size 2799969

README.md

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+# Frappe_x1
+
+The Frappe dataset contains a context-aware app usage log, which comprises 96203 entries by 957 users for 4082 apps used in various contexts. It has 10 feature fields including user_id, item_id, daytime, weekday, isweekend, homework, cost, weather, country, city. The target value indicates whether the user has used the app under the context. We provide the reusable, processed dataset released by [the BARS benchmark](https://openbenchmark.github.io), which are randomly split into 7:2:1 as the training set, validation set, and test set, respectively.
+
+### Dataset Details
+
++ **Repository:** https://github.com/reczoo/BARS/tree/main/datasets/Frappe#frappe_x1
+
++ **Used by papers:** 
+    - Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong. [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902). In AAAI 2023.
+    - Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang. [FINAL: Factorized Interaction Layer for CTR Prediction](https://dl.acm.org/doi/10.1145/3539618.3591988). In SIGIR 2023.
+    - Weiyu Cheng, Yanyan Shen, Linpeng Huang. [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://ojs.aaai.org/index.php/AAAI/article/view/5768). In AAAI 2020.
+
++ **Check the md5sum for data integrity:**
+      ```bash
+      $ md5sum train.csv valid.csv test.csv
+      ba7306e6c4fc19dd2cd84f2f0596d158 train.csv
+      88d51bf2173505436d3a8f78f2a59da8 valid.csv
+      3470f6d32713dc5f7715f198ca7c612a test.csv
+      ```