Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Collaborative Self-Attention Network for Session-based Recommendation
Authors: Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,822 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,688 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets show that Co SAN constantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Institute of AI, School of Computer Science and Technology, Soochow University, China 2Rutgers University, New Jersey, USA 3Key Lab of IIP of CAS, Institute of Computing Technology, Beijing, China 4The University of Chinese Academy of Sciences, Beijing, China 5School of Computer, Beihang University, Beijing, China 6Texas Tech University, Texas, USA |
| Pseudocode | No | The paper includes an architecture diagram (Figure 1) but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is open or publicly available. |
| Open Datasets | Yes | We study the effectiveness of our proposed model Co SAN on two real-world datasets, i.e., Retailrocket1 and Yoochoose2. ... 1https://www.kaggle.com/retailrocket/ecommerce-dataset 2http://2015.recsyschallenge.com/challege.html |
| Dataset Splits | Yes | We take the sessions of the subsequent day on Yoochoose and the sessions of the subsequent week on Retailrocket for testing. ... Since Yoochoose is quite large, we sorted the training sequences by time and reported our results on more recent fractions 1/64 and 1/4 of the training sequences [Li et al., 2017]. ... Table 1: Statistics of the datasets. (includes 'train' and 'test' rows with counts) |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Without a special mention, we set the number of self-attention heads h and self-attention layers r to 1 and 2 respectively. Also, the weighting parameter α is set to 0.5. |