Personalización de imágenes en la promoción de contenidos audiovisuales. El caso de Netflix.

Treballs Finals del Grau de Comunicació Audiovisual, Facultat de Biblioteconomia i Documentació, Universitat de Barcelona, Curs: 2017-2018, Tutor: Daniel Jariod DatoNetflix se ha convertido en líder del servicio de contenidos audiovisuales streaming de pago, con más de 80 millones de suscriptores y presencia en un centenar de países. Su éxito no es fortuito y se debe, entre muchos otros factores, al énfasis que pone en personalizar la experiencia de cada usuario. Después de varios estudios, la empresa se percató de que los thumbnails -la imagen promocional de las series y películas- no solo influían más que cualquier otro factor a la hora de escoger un producto, sino que además constituían un 82% del foco de atención cuando se navega por la plataforma. También constató que los usuarios permanecían una media de 1,8 segundos considerando cada ilustración que se les ofrecía. Se observó, de esta manera, que las imágenes eran un elemento esencial y que Netflix disponía de muy poco tiempo para captar el interés de sus suscriptores. A raíz de estos descubrimientos, la compañía concluyó que si personalizaba los thumbnails podía mejorar la experiencia de sus usuarios. El presente estudio toma como punto de partida esta hipótesis para reflexionar sobre cómo internet ha cambiado los hábitos de consumo de contenidos audiovisuales y cómo la personalización ha transformado los mecanismos de promoción de estos, pasando de un público único y global a uno individual y subjetivo. Se analiza el caso concreto de Netflix y se investiga hasta qué punto la compañía personaliza sus imágenes, qué métodos utiliza y las consecuencias ontológicas que pueden derivarse.Netflix has become the leader in streaming services of audiovisual content, with more than 80 million subscribers and presence in a hundred countries. Its success is not fortuitous and is due, among many other factors, to the emphasis it puts on personalizing the experience of each user. After several studies, the company realized that the thumbnails - the promotional image of the series and movies - not only influenced people more than any other factor when choosing a product, but also constituted 82% of the focus of attention while navigating the platform. They also found that users stayed an average of 1.8 seconds considering each illustration that was offered. It was observed, in this way, that the images were an essential element and that Netflix had very little time to attract the interest of its subscribers. As a result of these discoveries, the company concluded that personalizing the thumbnails could improve the experience of the users. The present study takes this hypothesis as a starting point to reflect on how the internet has changed the way we consume content and how personalization has transformed the promotional mechanisms of it, going from a single and global audience to an individual and subjective one. Netflix serves as a specific case to examine the extent to which images are personalized, what methods are used and the ontological consequences that can be derived

AdaptEx: A Self-Service Contextual Bandit Platform

This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous "cold start" situations gracefully

Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce

Homepage is the first touch point in the customer's journey and is one of the prominent channels of revenue for many e-commerce companies. A user's attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer's interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating wide variety of homepage banners that can be made as an input for user level personalization engine. Following are the main contributions of this paper: 1) We introduce and explain the need for large scale banner generation for e-commerce 2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. 3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. 4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra (http://www.myntra.com), one of the top fashion e-commerce players in India.Comment: Workshop on Recommender Systems in Fashion, 13th ACM Conference on Recommender Systems, 201

Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using visual content created by the users is one particularly promising option, showing a potential to maximize transparency and user trust. Existing models for explaining recommendations in this context face limitations: sustainability has been a critical concern, as they often require substantial computational resources, leading to significant carbon emissions comparable to the Recommender Systems where they would be integrated. Moreover, most models employ surrogate learning goals that do not align with the objective of ranking the most effective personalized explanations for a given recommendation, leading to a suboptimal learning process and larger model sizes. To address these limitations, we present BRIE, a novel model designed to tackle the existing challenges by adopting a more adequate learning goal based on Bayesian Pairwise Ranking, enabling it to achieve consistently superior performance than state-of-the-art models in six real-world datasets, while exhibiting remarkable efficiency, emitting up to 75% less CO${_2}$ during training and inference with a model up to 64 times smaller than previous approaches

Meta-Personalizing Vision-Language Models to Find Named Instances in Video

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as ``My dog Biscuit'' appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and DeepFashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.Comment: Accepted to CVPR 2023. Project webpage: https://danielchyeh.github.io/metaper

The Role of Book Covers in Shaping Visual Discourse: A Preliminary Observation on the Stereotyped Istanbul in the German-Speaking Book Market

Book covers as visual materials convey what reading audiences can expect from texts. A quick glance at covers of translated Turkish books, translations from other languages and also German books (fiction and nonfiction) in the German-speaking book market shows that covers of works containing the word “Istanbul” in their title are typically decorated with mosques and minarets. Taking this observation as a starting point, this paper aims to question the underlying motivations of this special case focusing on selected literary translations, non-literary works on Istanbul, and local literary productions written in German. The analysis indicates that Istanbul holds a predefined, fixed, and clear-cut image in the minds of professional and nonprofessional German-speaking readers.Las portadas de los libros, en su calidad visual, transmiten lo que el público lector puede esperar de los textos. Indican la visualidad discursiva que conecta los códigos culturales con las reacciones inconscientes. Un vistazo rápido a las portadas de libros turcos traducidos en el mercado del libro de habla alemana muestra que las portadas de las obras que contienen la palabra "Estambul" en su título están "decoradas" con mezquitas y minaretes. Partiendo de esta observación, el presente artículo se propone cuestionar las motivaciones subyacentes de este caso especial centrándose en una selección de traducciones literarias del turco al alemán, obras no literarias sobre Estambul y producciones literarias autóctonas escritas en alemán. El análisis indica que Estambul tiene una imagen predefinida, fija y clara en la mente de los lectores y profesionales de habla alemana.En tant que supports visuels, les couvertures de livres communiquent ce que les lecteurs peuvent attendre des textes. Elles indiquent la visualité discursive qui relie les codes culturels aux réactions inconscientes. Un coup d'œil sur les couvertures de livres turcs traduits sur le marché germanophone montre que celles qui contiennent le mot « Istanbul » dans leurs titres sont ornées de mosquées et de minarets. Partant de cette observation, le présent article vise à interroger les motivations sous-jacentes de ce phénomène en se penchant sur une sélection de traductions littéraires du turc vers l'allemand, d'ouvrages non littéraires sur Istanbul et de productions littéraires indigènes écrites en allemand. L'analyse démontre finalement qu'Istanbul détient une image prédéfinie, fixe et claire dans l'esprit des lecteurs et des professionnels germanophones.As capas dos livros como materiais visuais expressam o que o público leitor pode esperar dos textos. Elas indicam a visualidade discursiva que conecta os códigos culturais com as reações inconscientes. Uma olhada rápida para as capas dos livros turcos traduzidos para o mercado de livros de língua alemã mostra que as capas das obras contendo a palavra “Istambul” em seu título são “decoradas” com mesquitas e minaretes. Tendo esta observação como ponto de partida, este artigo visa questionar as motivações implícitas deste caso especial com foco nas traduções literárias selecionadas do turco para o alemão, obras não literárias sobre Istambul e produções literárias nativas escritas em alemão. A análise indica que Istambul guarda uma imagem pré-definida, fixa e clara na mente dos leitores e profissionais de língua alemã

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.Comment: Project page: https://anti-dreambooth.github.io

European audiovisual media policy in the age of global video on demand services: A case study of Netflix in the Netherlands

This article considers the provisions in the European Union's revised Audiovisual Media Services Directive concerning video on demand (VOD) services and the effectiveness of supply-side cultural diversity regulations in achieving their purported policy goals of increased production and consumption of European works. Because the Netherlands is the 'country of origin' to several multinational VOD services, including Netflix, we conducted a case study of this specific national context. We examine the quota for and prominence of European works, as well as different forms of financial obligations. We find that the former two policy tools may require new strategies to effectively reach their objectives in a nonlinear context. Our evidence also indicates that the latter remains controversial in the domestic audiovisual industry, as stakeholder positions are dependent on the type(s) of production stimulated. Based on this, we argue that securing the independence of producers and ensuring VOD services are transparent with respect to performance data are essential to promoting source diversity and a sustainable value chain

Using AI to personalise emotionally appealing advertisement

Personal data and information collected online by companies can be used to design and personalise advisements. This chapter extends existing research into the online behavioural advertising by proposing a model that incorpo-rates artificial intelligence and machine learning into developing emotionally appealing advertisements. It is proposed that big data and consumer analytics collected through AI from different sources, will be aggregated to have a bet-ter understanding of consumers as individuals. Personalised emotionally ap-pealing advertisements will be created with this information and shared digi-tally using pragmatic advertising strategies. Theoretically, this chapter con-tributes towards the use of emerging technologies such as AI and Machine Learning for Digital Marketing, big data acquisition, management and analyt-ics and its impact on advertising effectiveness. With customer analytics mak-ing up a more significant part of big data use in sales and marketing and GDPR ensures data are legitimately collected and processed, there are practi-cal implications for Managers as well. Acknowledging that this is a concep-tual model, the critical challenges are presented. This is open for future re-search and development both from academic, digital marketing practitioners and computer scientist

European audiovisual media policy in the age of global video on demand services: A case study of Netflix in the Netherlands

This article considers the provisions in the European Union's revised Audiovisual Media Services Directive concerning video on demand (VOD) services and the effectiveness of supply-side cultural diversity regulations in achieving their purported policy goals of increased production and consumption of European works. Because the Netherlands is the 'country of origin' to several multinational VOD services, including Netflix, we conducted a case study of this specific national context. We examine the quota for and prominence of European works, as well as different forms of financial obligations. We find that the former two policy tools may require new strategies to effectively reach their objectives in a nonlinear context. Our evidence also indicates that the latter remains controversial in the domestic audiovisual industry, as stakeholder positions are dependent on the type(s) of production stimulated. Based on this, we argue that securing the independence of producers and ensuring VOD services are transparent with respect to performance data are essential to promoting source diversity and a sustainable value chain