Bernoulli Rank-1 Bandits for Click Feedback
Authors: Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with synthetic data confirm that on benign instances the performance of Rank1Elim KL is significantly better than that of even Rank1Elim. Similarly, experiments with models derived from real-data confirm that the improvements are significant across the board, regardless of whether the data is benign or not. Our final contribution is the experimental validation of Rank1Elim KL, on both synthetic and real-world problems. |
| Researcher Affiliation | Collaboration | Sumeet Katariya 1, Branislav Kveton 2, Csaba Szepesv ari 3, Claire Vernade 4, Zheng Wen 2 1University of Wisconsin-Madison, 2Adobe Research, 3University of Alberta, 4Telecom Paris Tech |
| Pseudocode | Yes | The pseudocode of our algorithm, Rank1Elim KL, is in Algorithm 1. Algorithm 1 Rank1Elim KL for Bernoulli rank-1 bandits. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing their code for the described methodology. While it mentions 'open-source code' in the context of what other papers might provide, it does not do so for its own implementation. |
| Open Datasets | Yes | In this experiment, we compare Rank1Elim KL to other algorithms on click models that are trained on the Yandex dataset [Yandex, 2013], an anonymized search log of 35M search sessions. |
| Dataset Splits | No | The paper discusses synthetic data generation and uses a real-world dataset (Yandex) but does not specify any train/validation/test splits, percentages, or cross-validation setup for its experiments. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Following Katariya et al. [2017], we consider the needle in a haystack class of problems, where only one item is attractive and one position is examined. We recall the problem here. The i-th entry of ut, ut(i), and the j-th entry of vt, vt(j), are independent Bernoulli variables with means u(i) = p U + U1{i = 1} , v(j) = p V + V1{j = 1} , (8), for some (p U, p V) [0, 1]2 and gaps ( U, V) (0, 1 p U] (0, 1 p V]. Note that arm (1, 1) is optimal with an expected reward of (p U + U)(p V + V). The goal of this experiment is to compare Rank1Elim KL with five other algorithms from the literature and validate that its regret scales linearly with K and L, which implies that it exploits the problem structure. In this experiment, we set p U =p V =0.25, U = V =0.5,and K =L,sothatµ= (1 1/K)0.25+0.5/K,1 pmax =0.25, andγ=µ=0.25+0.5/K. |