Learning Parameters for a Generalized Vidale-Wolfe Response Model with
Flexible Ad Elasticity and Word-of-Mouth
- URL: http://arxiv.org/abs/2202.13566v1
- Date: Mon, 28 Feb 2022 06:31:02 GMT
- Title: Learning Parameters for a Generalized Vidale-Wolfe Response Model with
Flexible Ad Elasticity and Word-of-Mouth
- Authors: Yanwu Yang, Baozhu Feng, Daniel Zeng
- Abstract summary: The Vidale-Wolfe (GVW) model contains two useful indexes representing advertiser's elasticity and the word-of-mouth (WoM) effect, respectively.
We present a deep neural network (DNN)-based estimation method to learn its parameters.
The research outcome shows that both the ad elasticity index and the WoM index have significant influences on advertising responses.
- Score: 1.052782170493037
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this research, we investigate a generalized form of Vidale-Wolfe (GVW)
model. One key element of our modeling work is that the GVW model contains two
useful indexes representing advertiser's elasticity and the word-of-mouth (WoM)
effect, respectively. Moreover, we discuss some desirable properties of the GVW
model, and present a deep neural network (DNN)-based estimation method to learn
its parameters. Furthermore, based on three realworld datasets, we conduct
computational experiments to validate the GVW model and identified properties.
In addition, we also discuss potential advantages of the GVW model over
econometric models. The research outcome shows that both the ad elasticity
index and the WoM index have significant influences on advertising responses,
and the GVW model has potential advantages over econometric models of
advertising, in terms of several interesting phenomena drawn from practical
advertising situations. The GVW model and its deep learning-based estimation
method provide a basis to support big data-driven advertising analytics and
decision makings; in the meanwhile, identified properties and experimental
findings of this research illuminate critical managerial insights for
advertisers in various advertising forms.
Related papers
- Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data [35.229595049396245]
We propose a novel visual rejection sampling framework to improve the cognition and explainability of LMMs.
Our approach begins by synthesizing interpretable answers that include human-verifiable visual features.
After each round of fine-tuning, we apply a reward model-free filtering mechanism to select the highest-quality interpretable answers.
arXiv Detail & Related papers (2025-02-19T19:05:45Z) - Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities [4.389938747401259]
This work explores the effects of fine-tuning strategies on Large Language Models (LLMs) in domains such as materials science and engineering.
We find that the merging of multiple fine-tuned models can lead to the emergence of capabilities that surpass the individual contributions of the parent models.
arXiv Detail & Related papers (2024-09-05T11:49:53Z) - Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning [26.02988481241285]
Modern large language models (LLMs) produce disinformation with human-like quality.
diversity in prompting methods used to generate disinformation complicates accurate source attribution.
We introduce the concept of model attribution as a domain generalization problem.
arXiv Detail & Related papers (2024-07-31T00:56:09Z) - Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary.
We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
arXiv Detail & Related papers (2024-03-12T14:58:52Z) - Revealing Multimodal Contrastive Representation Learning through Latent
Partial Causal Models [85.67870425656368]
We introduce a unified causal model specifically designed for multimodal data.
We show that multimodal contrastive representation learning excels at identifying latent coupled variables.
Experiments demonstrate the robustness of our findings, even when the assumptions are violated.
arXiv Detail & Related papers (2024-02-09T07:18:06Z) - Learning Generalizable Models via Disentangling Spurious and Enhancing
Potential Correlations [28.38895118573957]
Domain generalization (DG) aims to train a model on multiple source domains to ensure that it can generalize well to an arbitrary unseen target domain.
Adopting multiple perspectives, such as the sample and the feature, proves to be effective.
In this paper, we focus on improving the generalization ability of the model by compelling it to acquire domain-invariant representations from both the sample and feature perspectives.
arXiv Detail & Related papers (2024-01-11T09:00:22Z) - StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized
Image-Dialogue Data [129.92449761766025]
We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning.
This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models.
Our research includes comprehensive experiments conducted on various datasets.
arXiv Detail & Related papers (2023-08-20T12:43:52Z) - MinT: Boosting Generalization in Mathematical Reasoning via Multi-View
Fine-Tuning [53.90744622542961]
Reasoning in mathematical domains remains a significant challenge for small language models (LMs)
We introduce a new method that exploits existing mathematical problem datasets with diverse annotation styles.
Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches.
arXiv Detail & Related papers (2023-07-16T05:41:53Z) - Incorporating Domain Knowledge in Deep Neural Networks for Discrete
Choice Models [0.5801044612920815]
This paper proposes a framework that expands the potential of data-driven approaches for DCM.
It includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment.
A case study demonstrates the potential of this framework for discrete choice analysis.
arXiv Detail & Related papers (2023-05-30T12:53:55Z) - Beyond Explaining: Opportunities and Challenges of XAI-Based Model
Improvement [75.00655434905417]
Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models.
This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models.
We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
arXiv Detail & Related papers (2022-03-15T15:44:28Z) - Meta-learning using privileged information for dynamics [66.32254395574994]
We extend the Neural ODE Process model to use additional information within the Learning Using Privileged Information setting.
We validate our extension with experiments showing improved accuracy and calibration on simulated dynamics tasks.
arXiv Detail & Related papers (2021-04-29T12:18:02Z) - DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive
Surveillance of COVID-19 Using Heterogeneous Features and their Interactions [2.30238915794052]
We propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days.
Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties.
arXiv Detail & Related papers (2020-07-31T23:37:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.