# Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling
## 4 The Datasets  
We applied the experimentation on four session-based datasets: Leather ([[https://github.com/delmarin35/Graph-Probability-Rec-Sys/tree/main/data](https://github.com/delmarin35/Graph-Probability-Rec-Sys/tree/main/data)]([https://github.com/delmarin35/Graph-Probability-Rec-Sys/tree/main/data](https://github.com/delmarin35/Graph-Probability-Rec-Sys/tree/main/data)), accessed on 19 November 2022), Yoochoose1/64 ([[http://2015.recsyschallenge.com/challege.html](http://2015.recsyschallenge.com/challege.html)]([http://2015.recsyschallenge.com/challege.html](http://2015.recsyschallenge.com/challege.html)), accessed on 3 April 2022), Diginetica ([[http://cikm2016.cs.iupui.edu/cikm-cup](http://cikm2016.cs.iupui.edu/cikm-cup)]([http://cikm2016.cs.iupui.edu/cikm-cup](http://cikm2016.cs.iupui.edu/cikm-cup)), accessed on 3 April 2022), eElectronics ([[https://www.kaggle.com/datasets/mkechnov/ecommerce-events-history-in-electronics-store](https://www.kaggle.com/datasets/mkechnov/ecommerce-events-history-in-electronics-store)]([https://www.kaggle.com/datasets/mkechnov/ecommerce-events-history-in-electronics-store](https://www.kaggle.com/datasets/mkechnov/ecommerce-events-history-in-electronics-store)), accessed on 18 June 2022). **Leather:** The data of the Leather dataset obtained from the processing of web server log records of an e-shop with leather apparel, jackets, furs and accessories. This is real data that emerged from the log files that were recorded implicitly during the users' navigation actions in the e-shop platform for the time period of six months from March to August of 2021. The log data were preprocessed by identifying sessions, session length, user actions in each session, and the items targeted by the actions. We consider as a session step every user action during the session, for example, viewing an item or adding an item to the basket. The dataset was processed to obtain only the sessions that contain at least two behavior sequences, _\"view item\"_ and _\"add to cart\"_, resulting in 102,024 records. The dataset  
Figure 3: In the RIC method, the item weight vector \(\mathbf{w}_{t}\) at any time step \(t\) is generated by a first order linear recurrent model. The input to the model is the current confidence vector \(\mathbf{c}_{t}\). was split into train (80%) and test (20%) sets. Thus, the number of records of the train and test sets are 81,651 and 20,373 respectively. The total number of unique sessions is 19,236 which corresponds to 15,388 unique sessions of the train set and 3848 unique sessions of the test set. In addition, the dataset contains 1448 unique items, of which 1429 appear in the train set and 1296 in the test set. The same item never appears in two consecutive steps in any session. **Yoochoose1/64:** It is a public benchmark session-based dataset that has been commonly used to evaluate recommendation system performance. This dataset has 17,740 unique items, of which 17,371 appear in the train set and 6745 in the test set. In addition, 369 items of the test set do not appear in the train set, while 10,995 train set items do not exist in the test set. Moreover, 16.183% of the item pairs in the train sessions and 14.938% of the item pairs in the test sessions, respectively, contain the same item twice. **Diginetica:** similarly to the Yoochoose1/64 dataset, the Diginetica dataset is often used as a benchmark for testing recommendation systems' performances. This dataset contains 43,097 items. All the items appear in the train set, but only 21,129 appear in the test set. The percentage of consecutive item pairs with a repeated item is less than in Yoochoose1/64, being approximately 9% in both train and test sets. **eElectronics:** this dataset contains user behavior data recorded for a period of 5 months (October 2019-February 2020) from a large electronics online store.