TopRank: A practical algorithm for online stochastic ranking
Authors: Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvari
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments We experiment with the Yandex dataset [15], a dataset of 167 million search queries. ... Top Rank is compared to Batch Rank [17] and Cascade KL-UCB [6]. ... The results are averaged over 10 runs. |
| Researcher Affiliation | Collaboration | Tor Lattimore Deep Mind Branislav Kveton Google Shuai Li The Chinese University of Hong Kong Csaba Szepesvári Deep Mind and University of Alberta |
| Pseudocode | Yes | Algorithm 1 Top Rank |
| Open Source Code | No | The paper mentions using 'Py Click' and the 'implementation of Batch Rank by Zoghi et al. [17]' but does not provide any links or explicit statements about releasing their own source code for Top Rank. |
| Open Datasets | Yes | We experiment with the Yandex dataset [15], a dataset of 167 million search queries. ... [15] Yandex. Yandex personalized web search challenge. https://www.kaggle.com/c/yandexpersonalized-web-search-challenge, 2013. |
| Dataset Splits | No | The paper mentions selecting '60 frequent search queries' and learning 'CMs and PBMs' but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using 'Py Click' and an 'implementation of Batch Rank', but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Our goal is to rerank L = 10 most attractive items with the objective of maximizing the expected number of clicks at the first K = 5 positions. This is the same experimental setup as in Zoghi et al. [17]. ... The parameter δ in Top Rank is set as δ = 1/n, as suggested in Theorem 1. |