Hasil untuk "Advertising"

Edson C. Tandoc, Zheng Wei Lim, Richard Ling

Xinran He, Junfeng Pan, Ou Jin et al.

Yeosun Yoon, Zeynep Gürhan‐Canli, N. Schwarz

D. Clarke

Binxiao Xu, Junyu Feng, Xiaopeng Lin et al.

Multimodal understanding of advertising videos is essential for interpreting the intricate relationship between visual storytelling and abstract persuasion strategies. However, despite excelling at general search, existing agents often struggle to bridge the cognitive gap between pixel-level perception and high-level marketing logic. To address this challenge, we introduce AD-MIR, a framework designed to decode advertising intent via a two-stage architecture. First, in the Structure-Aware Memory Construction phase, the system converts raw video into a structured database by integrating semantic retrieval with exact keyword matching. This approach prioritizes fine-grained brand details (e.g., logos, on-screen text) while dynamically filtering out irrelevant background noise to isolate key protagonists. Second, the Structured Reasoning Agent mimics a marketing expert through an iterative inquiry loop, decomposing the narrative to deduce implicit persuasion tactics. Crucially, it employs an evidence-based self-correction mechanism that rigorously validates these insights against specific video frames, automatically backtracking when visual support is lacking. Evaluation on the AdsQA benchmark demonstrates that AD-MIR achieves state-of-the-art performance, surpassing the strongest general-purpose agent, DVD, by 1.8% in strict and 9.5% in relaxed accuracy. These results underscore that effective advertising understanding demands explicitly grounding abstract marketing strategies in pixel-level evidence. The code is available at https://github.com/Little-Fridge/AD-MIR.

Junwei Pan, Wei Xue, Chao Zhou et al.

Generative recommender systems are rapidly emerging as a new paradigm for recommendation, where collaborative identifiers and/or multi-modal content are mapped into discrete token spaces and user behavior is modelled with autoregressive sequence models. Despite progress on multi-modal recommendation datasets, there is still a lack of public benchmarks that jointly offer large-scale, realistic and fully all-modality data designed specifically for generative recommendation (GR) in industrial advertising. To foster research in this direction, we organised the Tencent Advertising Algorithm Challenge 2025, a global competition built on top of two all-modality datasets for GR: TencentGR-1M and TencentGR-10M. Both datasets are constructed from real de-identified Tencent Ads logs and contain rich collaborative IDs and multi-modal representations extracted with state-of-the-art embedding models. The preliminary track (TencentGR-1M) provides 1 million user sequences with up to 100 interacted items each, where each interaction is labeled with exposure and click signals, while the final track (TencentGR-10M) scales this to 10 million users and explicitly distinguishes between click and conversion events at both the sequence and target level. This paper presents the task definition, data construction process, feature schema, baseline GR model, evaluation protocol, and key findings from top-ranked and award-winning solutions. Our datasets focus on multi-modal sequence generation in an advertising setting and introduce weighted evaluation for high-value conversion events. We release our datasets at https://huggingface.co/datasets/TAAC2025 and baseline implementations at https://github.com/TencentAdvertisingAlgorithmCompetition/baseline_2025 to enable future research on all-modality generative recommendation at an industrial scale. The official website is https://algo.qq.com/2025.

Shuo Lu, Haohan Wang, Wei Feng et al.

Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness. To bridge this gap, we present \textit{One Size, Many Fits} (OSMF), a unified framework that aligns diverse group-wise click preferences in large-scale advertising image generation. OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics, representing each group with rich collective preference features. Building on these groups, preference-conditioned image generation employs a Group-aware Multimodal Large Language Model (G-MLLM) to generate tailored images for each group. The G-MLLM is pre-trained to simultaneously comprehend group features and generate advertising images. Subsequently, we fine-tune the G-MLLM using our proposed Group-DPO for group-wise preference alignment, which effectively enhances each group's CTR on the generated images. To further advance this field, we introduce the Grouped Advertising Image Preference Dataset (GAIP), the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings. Our code and datasets will be released at https://github.com/JD-GenX/OSMF.

Robert Kubinec, Aseem Mahajan

In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that combines large language models, human coders, and advances in Bayesian item response theory. We show that what is called greenwashing has diverse actors and components, but we also identify a very pernicious form, which we call political greenwashing, that appears to be promoted by fossil fuel companies and related interest groups. Based on ad targeting data, we show that much of this advertising happens via organizations with undisclosed links to the fossil fuel industry. Furthermore, we show that greenwashing ad content is being micro-targeted at left-leaning communities with fossil fuel assets, though we also find comparatively little evidence of ad targeting aimed at influencing public opinion at the national level.

Guangyi Zhang, Ilie Sarpe, Aristides Gionis

Content feeds provided by platforms such as X (formerly Twitter) and TikTok are consumed by users on a daily basis. In this paper, we revisit the native advertising problem in content feeds, initiated by Ieong et al. Given a sequence of organic items (e.g., videos or posts) relevant to a user's interests or to an information search, the goal is to place ads within the organic content so as to maximize a reward function (e.g., number of clicks), while accounting for two considerations: (1) an ad can only be inserted after a relevant content item; (2) the users' attention decays after consuming content or ads. These considerations provide a natural model for capturing both the advertisement effectiveness and the user experience. In this paper, we design fast and practical 2-approximation greedy algorithms for the associated optimization problem, improving over the best-known practical algorithm that only achieves an approximation factor of~4. Our algorithms exploit a counter-intuitive observation, namely, while top items are seemingly more important due to the decaying attention of the user, taking good care of the bottom items is key for obtaining improved approximation guarantees. We then provide the first comprehensive empirical evaluation on the problem, showing the strong empirical performance of our~methods.

Soumik Dey, Benjamin Braun, Naveen Ravipati et al.

E-commerce sellers are advised to bid on keyphrases to boost their advertising campaigns. These keyphrases must be relevant to prevent irrelevant items from cluttering Search systems and to maintain positive seller perception. It is vital that keyphrase suggestions align with seller, Search, and buyer judgments. Given the challenges in collecting negative feedback in these systems, LLMs have been used as a scalable proxy for human judgments. We present an empirical study on a major e-commerce platform of a distillation framework involving an LLM teacher, a cross-encoder assistant and a bi-encoder Embedding Based Retrieval (EBR) student model, aimed at mitigating click-induced biases and provide more diverse keyphrase recommendations while aligning advertising, search and buyer preferences.

Hannah Krahl, Katrin Hartwig, Ann-Kathrin Fischer et al.

Mobile gaming apps are woven into children's daily lives. Given their ongoing cognitive and emotional development, children are especially vulnerable and depend on designs that safeguard their well-being. When apps feature manipulative interfaces or heavy advertising, they may exert undue influence on young users, contributing to prolonged screen time, disrupted self-regulation, and accidental in-app purchases. In this study, we examined 20 popular, free-to-download children's apps in German-speaking regions to assess the prevalence of deceptive design patterns and advertising. Despite platform policies and EU frameworks like the General Data Protection Regulation and the Digital Services Act, every app contained interface manipulations intended to nudge, confuse, or pressure young users, averaging nearly six distinct deceptive patterns per app. Most also displayed high volumes of non-skippable ads, frequently embedded within core gameplay. These findings indicate a systemic failure of existing safeguards and call for stronger regulation, greater platform accountability, and child-centered design standards.

Miguel Antonio Sánchez Flores, Mauricio Capacyachi Trivelli

La historiografía oficial peruana ha señalado constantemente los fines de la década de los cincuenta como los primeros años del diseño gráfico profesional en el Perú. Caracterizada por la ampliación comercial de las grandes economías en el país, la década, además, afianzó la llegada de artistas gráficos extranjeros a Lima y también la aparición de instituciones educativas vinculadas con la enseñanza de la disciplina. De ese modo, los acercamientos a estos «primeros años» han priorizado, sobre todo, el relato «modernizante» de la influencia extranjera como parte de su consolidación como práctica profesional, y han eludido otras tradiciones como la influencia precolombina o el trabajo predecesor de grandes artistas como Elena Izcue y Julia Codesido. Sin duda, la presencia de agencias de publicidad en la mitad del siglo XX y también la llegada al Perú de diseñadores sobre todo suizos colaboraron con la conformación profesional de una primera generación de diseñadores gráficos peruanos, quienes no solo desarrollaron visualidad gráfica para empresas y marcas, sino que también incorporaron nuevos referentes y técnicas a su práctica. El presente artículo, a partir de cinco entrevistas con algunos representantes de esta «primera generación», indaga en la importancia de la tradición extranjera para el desarrollo de la práctica profesional y también analiza la presencia de la influencia precolombina en sus trabajos. El texto concluye que la mayoría de los entrevistados destacan la influencia extranjera —específicamente de la tradición suiza— como punto de partida de la práctica profesional en el Perú; sin embargo, advierten también la importancia de la tradición precolombina, mayoritariamente utilitaria para su trabajo.

Abdullah Ballı

Bu çalışmada, pazarlama mesajlarının tüketici davranışları üzerindeki etkilerini anlamak için kişilik kuramları, sosyal öğrenme teorisi ve psikanalitik yaklaşım gibi teorik çerçeveler kullanılmıştır. Araştırmanın amacı, reklamların bilinçli ve bilinçdışı algılar yoluyla davranışsal sonuçlara nasıl dönüştüğünü değerlendirmektir. Çalışmada, nitel bir yöntem benimsenmiş ve Apple Iphone’nin “The Greatest” başlıklı reklam filmi incelenmiştir. İlk defa 2022 yılında izleyiciyle buluşan reklam filmi araştırma kapsamında 01 Eylül-30 Eylül 2024 tarihleri arasında analiz edilmiştir. Reklamın içeriği, kullanılan semboller ve iletilen mesajlar teorik çerçeveler doğrultusunda analiz edilmiştir. Bulgular, reklamın klasik ve edimsel koşullanma, sosyal öğrenme ve psikanalitik yaklaşımlar aracılığıyla tüketicilerde olumlu bir marka algısı yaratmayı hedeflediğini göstermektedir. Sembolik mesajların bilinçdışı güdüleri harekete geçirerek ürünleri kişisel ifade araçları olarak sunduğu tespit edilmiştir. Analiz sonuçları, Apple’ın duygusal bağlılık yaratarak marka sadakatini artırmayı amaçladığını ortaya koymaktadır. Araştırma, modern pazarlama stratejilerinin psikolojik ve davranışsal etkilerine dair önemli bir perspektif sunmaktadır.

E. A. Korduban

Background. Wellness and medical tourism are important areas in the tourism and healthcare industry, which are actively developing around the world. Both types of tourism are aimed at improving the health and overall well-being of travelers, but there are important differences between them regarding goals, treatment methods, and travel arrangements. Understanding these differences and similarities allows to more accurately identify the target groups of tourists and also contributes to the development of strategies for the successful development of these areas in the fields of tourism and healthcare.The purpose of the study is to consider approaches to the definition of the concepts of medical and health tourism and to identify their common and distinctive features that affect the use of terminology in scientific and practical research.Materials and methods. The study used content analysis of scientific literature on the issues of wellness and medical tourism, as well as methods of grouping and generalization. Medical tourism should be considered a type of tourism for the purpose of obtaining medical care without using natural healing resources.Results. The scientific research in medical tourism demonstrates a high degree of heterogeneity of methodological approaches to the definition of the most basic concept that delineates the object and subject of study. The primary shared characteristics of these sectors encompass a focus on health improvement, specialized infrastructure, the nature of the target audience, marketing and advertising, the availability of international quality standards, integration with tourism infrastructure, the impact on health and quality of life, and the participation of qualified personnel. The main differences can be determined through the following parameters: target audience groups, risk and degree of medical intervention, legal and insurance aspects, financial motivation, duration and nature of recovery, goals and expectations from the trip, treatment methods and procedures, and financial aspects.

A. Resnik, Bruce L. Stern

Xiang-Rong Sheng, Feifan Yang, Litong Gong et al.

Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models. In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. We start from identifying the key challenges in adopting multimodal data in a manner that is both effective and cost-efficient for industrial systems. To address these challenges, we introduce a two-phase framework, including: 1) the pre-training of multimodal representations to capture semantic similarity, and 2) the integration of these representations with existing ID-based models. Furthermore, we detail the architecture of our production system, which is designed to facilitate the deployment of multimodal representations. Since the integration of multimodal representations in mid-2023, we have observed significant performance improvements in Taobao display advertising system. We believe that the insights we have gathered will serve as a valuable resource for practitioners seeking to leverage multimodal data in their systems.

Dildar Ali, Suman Banerjee, Yamuna Prasad

Nowadays, billboard advertising has emerged as an effective advertising technique due to higher returns on investment. Given a set of selected slots and tags, how to effectively assign the tags to the slots remains an important question. In this paper, we study the problem of assigning tags to the slots such that the number of tags for which influence demand of each zone is satisfied gets maximized. Formally, we call this problem the Multi-Slot Tag Assignment Problem. The input to the problem is a geographical region partitioned into several zones, a set of selected tags and slots, a trajectory, a billboard database, and the influence demand for every tag for each zone. The task here is to find out the assignment of tags to the slots, such the number of tags for which the zonal influence demand is satisfied is maximized. We show that the problem is NP-hard, and we propose an efficient approximation algorithm to solve this problem. A time and space complexity analysis of the proposed methodology has been done. The proposed methodology has been implemented with real-life datasets, and a number of experiments have been carried out to show the effectiveness and efficiency of the proposed approach. The obtained results have been compared with the baseline methods, and we observe that the proposed approach leads to a number of tags whose zonal influence demand is satisfied.

Chang Zhou, Yang Zhao, Jin Cao et al.

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.

D. Aaker, Douglas M. Stayman, M. Hagerty

R. Batra, Douglas M. Stayman