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 [1].
Fatigue-Aware Bandits for Dependent Click Models
Authors: Junyu Cao, Wei Sun, Zuo-Jun (Max) Shen, Markus Ettl3341-3348
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform four sets of numerical experiments to evaluate the performance of our online learning algorithms. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley, California 94720 2IBM Research, Yorktown Height, New York 10591 |
| Pseudocode | Yes | Algorithm 1: Determine the optimal sequence S to the of๏ฌine combinatorial problem |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | The data is from Taobao, which contains 26 million ad display and click logs from 1,140,000 randomly sampled users from the website of Taobao for 8 days (5/6/2017-5/13/2017). https://tianchi.aliyun.com/datalab/data Set.html?spm=5176. 100073.0.0.14d53ea7Rleuc9&dataId=56 |
| Dataset Splits | No | The paper mentions using data from Taobao but does not provide specific details on how it was split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any 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. |
| Experiment Setup | Yes | We consider a setting with three categories, and each contains 10 items. Items intrinsic relevance u is uniformly generated from [0, 0.5]. The known discount factor is set to f(r) = exp( 0.1 (r 1)). We ๏ฌx the resuming probability after non-clicks to q = 0.7, and compare three cases by varying the resuming probabilities after clicks, i.e, Case 1: g = 0.95; Case 2: g = 0.85; Case 3: g = 0.75. For each case, we run 20 independent simulations. |