Online Optimization of Video-Ad Allocation

Authors: Hanna Sumita, Yasushi Kawase, Sumio Fujita, Takuro Fukunaga

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We evaluate performance of Algorithm 1 through computational experiments.
Researcher Affiliation Collaboration National Institute of Informatics Tokyo Institute of Technology RIKEN AIP Center Yahoo! JAPAN Research
Pseudocode Yes Algorithm 1: Online algorithm for allocating outcomes
Open Source Code No The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described.
Open Datasets No The paper uses 'randomly generated instances' and a 'real dataset' from 'Yahoo! JAPAN', which is proprietary. No concrete access information (link, DOI, formal citation) is provided for a publicly available or open dataset.
Dataset Splits No The paper describes an online optimization problem and evaluates algorithms based on the number of users arriving. It does not refer to standard training, validation, or test dataset splits in the context of machine learning model training.
Hardware Specification No The paper states 'the total computational times are short enough' but does not specify any hardware details like GPU/CPU models, memory, or specific computing environments used for the experiments.
Software Dependencies No The paper does not mention any specific software dependencies, libraries, or solvers with version numbers.
Experiment Setup Yes Each instance is generated as follows. We choose the number n of advertisers from 25, 50, and 100, and the number m of users from 500, 1000, and 2000. We assume two distributions of budgets: (1) uniform distribution (Bi = 200 for all i N), or (2) Pareto distribution... Each bid bij is picked uniformly at random from [0, 3]... Each ti is generated uniformly at random from [10, 45], and capacity Tj from [10, 60].