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..
Building Personalized Simulator for Interactive Search
Authors: Qianlong Liu, Baoliang Cui, Zhongyu Wei, Baolin Peng, Haikuan Huang, Hongbo Deng, Jianye Hao, Xuanjing Huang, Kam-Fai Wong
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results based on real-world dataset demonstrate the effectiveness of our agent and personalized simulator. and 5 Experiments 5.1 Dataset and Abstract Query 5.2 Experiment Setup 5.3 Experimental Results |
| Researcher Affiliation | Collaboration | 1Fudan University, China 2Alibaba Group, China 3The Chinese University of Hong Kong, Hong Kong 4Tianjin University, China |
| Pseudocode | Yes | Algorithm 1: Building real experience buffer with abstract query., Algorithm 2: Rollout Simulation, and Algorithm 3: Training Algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the open-sourcing of the code for the described methodology. |
| Open Datasets | No | Our dataset is derived from the log of Taobao APP 2, which is processed into transition tuple, i.e., (s, a, r, s , trmt). 2The largest E-commerce platform in China. |
| Dataset Splits | Yes | Table 1: Description of our dataset, of which 60% for training, 20% for testing, 20% for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'word2vec' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Implementation Details. The simulator is pre-trained before interacting with our agent and fixed unchanged while interacting with the agent. Each word is represented by word embedding (200-dim) trained on the historical queries from other scenarios of Taobao APP ( 12.7 million queries, 332,922 words) via word2vec. ϵ = 0.2, γ = 0.9, m = 5, N s = 5, T = 20, learning rate is set to 10 5 and 10 3 for the training of environment simulator and agent respectively. At each turn, the agent recommends 3 tags to the user (i.e., K = 3). The target network of the agent is updated every 200 steps. The hidden size (both bidirectional LSTM and fully connected layers) of simulator and agent is 5 and 10. The length of simulated experiences buffer Ds is 1000. |