Optimal Bidding Strategy for Brand Advertising
Authors: Takanori Maehara, Atsuhiro Narita, Jun Baba, Takayuki Kawabata
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. takanori.maehara@riken.jp, narita_atsuhiro@cyberagent.co.jp, baba_jun@cyberagent.co.jp, kawabata_takayuki@cyberagent.co.jp |
| 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. |