DCM Bandits: Learning to Rank with Multiple Clicks
Authors: Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on synthetic and realworld problems, and show that it performs well even when our model is misspecified. This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model. We conduct three experiments. In Section 5.1, we validate that the regret of dcm KL-UCB scales as suggested by Theorem 1. In Section 5.2, we compare dcm KL-UCB to multiple baselines. Finally, in Section 5.3, we evaluate dcm KL-UCB on a real-world dataset. |
| Researcher Affiliation | Collaboration | Sumeet Katariya KATARIYA@WISC.EDU Department of Electrical and Computer Engineering, University of Wisconsin-Madison Branislav Kveton KVETON@ADOBE.COM Adobe Research, San Jose, CA Csaba Szepesv ari SZEPESVA@CS.UALBERTA.CA Department of Computing Science, University of Alberta Zheng Wen ZWEN@ADOBE.COM Adobe Research, San Jose, CA |
| Pseudocode | Yes | Algorithm 1 dcm KL-UCB for solving DCM bandits. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code for the methodology is openly available or included in supplementary materials. |
| Open Datasets | Yes | We evaluate dcm KL-UCB on the Yandex dataset (Yandex), a search log of 35M search sessions. Yandex. Yandex personalized web search challenge. https://www.kaggle.com/c/yandex-personalized-web-search-challenge, 2013. |
| Dataset Splits | No | The paper mentions using synthetic problems and the Yandex dataset but does not provide specific details on dataset splits (percentages, sample counts, or explicit splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper discusses the experimental setup and results but does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and models but does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') that would be needed to replicate the experiment. |
| Experiment Setup | No | The paper describes high-level experimental conditions (e.g., varying L, K, gamma, using 20 most frequent queries), but it does not provide specific hyperparameter values or detailed system-level training settings needed for replication. |