Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science pape

Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives

With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have much higher prediction performance than conventional machine learning models, the specific prediction process is still difficult to interpret and/or explain. This is known as the black-boxing of machine learning models and is recognized as a particularly important problem in a wide range of research fields, including manufacturing, commerce, robotics, and other industries where the use of such technology has become commonplace, as well as the medical field, where mistakes are not tolerated.Focusing on natural language processing tasks, we consider interpretability as the presentation of the contribution of a prediction to an input word in a recurrent neural network. In interpreting predictions from deep learning models, much work has been done mainly on visualization of importance mainly based on attention weights and gradients for the inference results. However, it has become clear in recent years that there are not negligible problems with these mechanisms of attention mechanisms and gradients-based techniques. The first is that the attention weight learns which parts to focus on, but depending on the task or problem setting, the relationship with the importance of the gradient may be strong or weak, and these may not always be strongly related. Furthermore, it is often unclear how to integrate both interpretations. From another perspective, there are several unclear aspects regarding the appropriate application of the effects of attention mechanisms to real-world problems with large datasets, as well as the properties and characteristics of the applied effects. This dissertation discusses both basic and applied research on how attention mechanisms improve the performance and interpretability of machine learning models.From the basic research perspective, we proposed a new learning method that focuses on the vulnerability of the attention mechanism to perturbations, which contributes significantly to prediction performance and interpretability. Deep learning models are known to respond to small perturbations that humans cannot perceive and may exhibit unintended behaviors and predictions. Attention mechanisms used to interpret predictions are no exception. This is a very serious problem because current deep learning models rely heavily on this mechanism. We focused on training techniques using adversarial perturbations, i.e., perturbations that dares to deceive the attention mechanism. We demonstrated that such an adversarial training technique makes the perturbation-sensitive attention mechanism robust and enables the presentation of highly interpretable predictive evidence. By further extending the proposed technique to semi-supervised learning, a general-purpose learning model with a more robust and interpretable attention mechanism was achieved.From the applied research perspective, we investigated the effectiveness of the deep learning models with attention mechanisms validated in the basic research, are in real-world applications. Since deep learning models with attention mechanisms have mainly been evaluated using basic tasks in natural language processing and computer vision, their performance when used as core components of applications and services has often been unclear. We confirm the effectiveness of the proposed framework with an attention mechanism by focusing on the real world of applications, particularly in the field of computational advertising, where the amount of data is large, and the interpretation of predictions is necessary. The proposed frameworks are new attempts to support operations by predicting the nature of digital advertisements with high serving effectiveness, and their effectiveness has been confirmed using large-scale ad-serving data.In light of the above, the research summarized in this dissertation focuses on the attention mechanism, which has been the focus of much attention in recent years, and discusses its potential for both basic research in terms of improving prediction performance and interpretability, and applied research in terms of evaluating it for real-world applications using large data sets beyond the laboratory environment. The dissertation also concludes with a summary of the implications of these findings for subsequent research and future prospects in the field.博士（工学）法政大学 (Hosei University

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

Essays on Social Media and Digital Marketing

Digital technology is rapidly reshaping the way how brands interact with consumers. More and more marketers are shifting their focus from traditional marketing channels (e.g., TV) to digital channels (e.g., social media platforms). Effective targeting is key to successful social media and digital marketing campaigns. This dissertation seeks to shed light on who and how to target on social media platforms. The first chapter aims to provide insights on how to target customers who are connected to each other on social media platforms. We investigate how the network embeddedness (i.e., number of common followees, common followers, and common mutual followers) between two users impacts information diffusion from one (sender) to another (receiver). By analyzing the sharing of sponsored ads on Digg and brand-authored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. A receiver is more likely to share content from a sender if they share more common followees. A receiver is also more likely to share content if she shares more common followers and common mutual followers with the sender. However, this effect is moderated by the novelty of information. The second chapter strives to understand what affects paid endorsers’ participation and effectiveness in social advertising campaigns. We conduct a field experiment with an invitation design in which we manipulate both incentives and a soft eligibility requirement to participate in the campaign. There are three main findings from our analysis. (1) Payments higher than the average reward a potential endorser received in the past (gains) do not increase participation, whereas lower payments (losses) decrease participation. Neither gains nor losses compared to past reward affect performance. (2) Potential endorsers who are more likely to participate tend to be less effective. (3) Which characteristics are associated with effectiveness depends on whether success is measured in likes, comments, or retweets. For marketing managers, our findings provide insights on how to target customers in a directed network at a micro level and how to improve social advertising campaigns by better targeting and incenting potential endorsers

Essays on Customer Engagement Strategies and Tactics in Business and Consumer Markets

In the last decade, customer engagement has become a key topic for both practitioners and researchers. Classically, customer engagement deals with customer behavior beyond purchase and thus non-monetary contributions by the customer, such as Word-of-Mouth (WOM), feedback and online reviews, or participation in the innovation process. While previous literature largely focused on the conceptualization of customer engagement itself, only a few studies have investigated how managers can actually stimulate and/or facilitate customer engagement. However, the latter is of high importance since only a few customers are truly engaged and it is often left to the firm to take the initiative to engage the customer. Thus, marketers need to understand how to design and successfully implement customer engagement initiatives. Accordingly, this dissertation investigates customer engagement strategies and tactics. While customer engagement strategy pertains to the overarching plan to leverage customer engagement to achieve the firm’s goals, customer engagement tactics deal with single actions taken by the firm to facilitate customer engagement across the various touchpoints in the customer journey. Specifically, this dissertation includes three essays, each addressing distinct questions with respect to customer engagement over the customer journey. Specifically, the first essay is conceptual in nature and provides an analysis of the strategic relevance of customer engagement in business-to-business (B2B) markets. The second essay explores how industrial firms can leverage service touchpoints as opportunities to engage their B2B customers in the post-purchase phase by employing the field service force for cross- and up-selling. Finally, the third essay investigates how marketers can use executional content cues in their TV advertisings (e.g., informativeness, creativity, or branding) to engage consumers and mitigate zapping behavior. Both empirical studies are based on unique datasets of real-world engagement tactics and related customer behavior obtained from co-operating companies

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

Online advertisement morphing : empirical and strategic implications

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 100-103).Today's age of information centric globalization over the Internet requires customer awareness by not only good content communication, but also trust and empathy. Trust and Empathy can be generated only when the sellers understand customers. This is only possible when sellers are aware about how the customers conceive the advertisement presented to them over the web. Fortunately, this knowledge is facilitated by analyzing customer buying behaviour and understanding the cognitive behaviour of the customer using cognitive engines, stochastic measures and analytics. My research will focus towards empirical substantiation of the affects and implications of Morphing. The study includes methodologies that corporate world can formulate to develop strategic measures to target potential customers based on individual cognitive styles. The study also includes an analysis of the online advertising industry trends, interviews & perspectives of industry thought leaders, and business models of the future.by Nabeel A. Siddiqui.S.M