Efficient Explorative Key-Term Selection Strategies for Conversational Contextual Bandits
Authors: Zhiyong Wang, Xutong Liu, Shuai Li, John C. S. Lui
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real-world data show significant advantages of our algorithms in learning accuracy (up to 54% improvement) and computational efficiency (up to 72% improvement), compared to the classic Con UCB algorithm, showing the potential benefit to recommender systems. |
| Researcher Affiliation | Academia | 1The Chinese University of Hong Kong ,Hong Kong SAR, China 2Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1: General Con Lin UCB framework |
| Open Source Code | Yes | Codes are available at https://github.com/ZhiyongWangWzy/ConLinUCB. |
| Open Datasets | Yes | Last.FM is a dataset for artist recommendations containing 186,479 interaction records between 1,892 users and 17,632 artists. Movielens is a dataset for movie recommendation containing 47,957 interaction records between 2,113 users and 10,197 movies. ... The data is generated following (Li et al. 2019; Zhang et al. 2020; Wu et al. 2021). |
| Dataset Splits | No | The paper describes the total number of rounds (T) for the experiments and how the datasets were used, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers required for reproducibility. |
| Experiment Setup | Yes | Following (Zhang et al. 2020), we set T = 1,000, b(t) = 5 log(t+1) and |At| = 50, unless otherwise stated. ... We set b(t) = fq log t and vary fq to change the conversation frequencies, i.e., fq {5, 10, 20, 30}. We vary |At| to be 25, 50, 100, 200, 500. |