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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimal Bidding Strategy for Brand Advertising
Authors: Takanori Maehara, Atsuhiro Narita, Jun Baba, Takayuki Kawabata
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the proposed objective function and the algorithm in a real-world data collected from our system and a questionnaire survey. We observed that our objective function is reasonable in real-world setting, and the proposed algorithm outperformed the baseline online algorithms. |
| Researcher Affiliation | Collaboration | Takanori Maehara1,2, Atsuhiro Narita2, Jun Baba2, Takayuki Kawabata2 1 RIKEN Center for Advanced Intelligence Project 2 Cyber Agent Inc. EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for online knapsack constrained monotone submodular maximization problem. [...] Algorithm 2 Algorithm for bid optimization problem. |
| Open Source Code | No | The paper states 'All codes were implemented in Python 3.5.' but does not provide any link or explicit statement about making the source code publicly available. |
| Open Datasets | No | The datasets used in the experiments are collected by our real-time bidding system. [...] The first day s data, which comprises of 1,440,641 impressions with 731,029 unique users, is used for a training set and the second day s data [...] is used for an evaluation set. |
| Dataset Splits | Yes | The first day s data, which comprises of 1,440,641 impressions with 731,029 unique users, is used for a training set and the second day s data, which comprises of 1,455,002 impressions with 735,560 unique users (including the users who appeared in the first day s data) is used for an evaluation set. |
| Hardware Specification | Yes | The program was executed in a standard laptop computer (2.3GHz CPU, 8.0GB Memory). |
| Software Dependencies | No | The paper states 'All codes were implemented in Python 3.5. For the numerical integration, we use the adaptive Gauss Lobatto integrator, implemented in scipy.integrate.' While Python has a version, scipy.integrate does not have a version explicitly stated, and it's not a list of multiple key components with their versions. |
| Experiment Setup | Yes | For the purpose of the experiments, we determined the shape of the forgetting curve. We used the same forgetting curve function for all users as pj(t) = p0(t tj), where p0 is a function estimated in (4.1) where the constant term is ignored since the constant term can be understood as the fraction of people who already know the advertisement. |