Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation
Authors: Xiao Wang, Tingting Dai, Qiao Liu, Shuang Liang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three real-world datasets exhibit that Hear Int achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Xiao Wang , Tingting Dai , Qiao Liu and Shuang Liang University of Electronic Science and Technology of China EMAIL, ttdai EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and datasets are publicly available on Git Hub 1. 1https://github.com/jarviswww/Code4Hear Int |
| Open Datasets | Yes | We evaluate the proposed model on three benchmark datasets, namely Tmall 2, Retail Rocket3,Diginetica4. ... 2https://tianchi.aliyun.com/dataset/dataDetail?dataId=42 3https://www.kaggle.com/retailrocket/ecommerce-dataset 4https://competitions.codalab.org/competitions/11161 |
| Dataset Splits | No | The paper describes the training and test sets but does not specify a separate validation split, its size, or the method for creating it. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch versions, or specific libraries). |
| Experiment Setup | Yes | For the general setting, the embedding size is 100, the batch size is 100. For Hear Int, the initial learning rate is 0.001, which will decay by 0.6 after every 1 epoch. We employ the k-means as the clustering algorithm and set the number of clusters to 100. The threshold α of cosine similarity is 0 in three datasets. Mask probability β is 0.4 for Tmall , while 0.1 is for Diginetica and Retailrocket. The θ The best number of layers in both attention and GNN encoders is 1,2,2 for Tmall, Retailrocket, and Diginetica, respectively. |