With the popularity of various forms of E-commerce, web advertising has become one prominent channel that businesses use to reach out to the customers. It leverages the Internet to promote products and services to audiences, which also has been an important revenue source of many Internet companies such as online social media platforms and search engines.
    AI techniques have been extensively used in the pipeline of a web advertising system, such as retrieval, ranking and bidding. Despite the remarkable progress, there are still many unsolved and emerging issues about applying the state-of-the-art AI techniques to Web Advertising, such as the “cold-start” problem; trade-off between online AI systems serving accuracy and efficiency; data privacy protection and big data management.
    This workshop is targeted on the above and other relevant issues, aiming to create a platform for people from academia and industry to communicate their insights and recent results.

Chen Luo, Amazon.com

Towards Scalable, Unbiased, and Interactive Product Search

Product search engines like Amazon Search provide online shopping services for billions of people worldwide. Our goal at Amazon Search is to provide a fast, unbiased, and enjoyable shopping experience for our customers. In this talk, I will share our recent research achievements in making product search scalable, unbiased, and interactive. I will introduce (1) how to scaling up search by using randomized algorithms, (2) how to do unbiased online learning to rank, and (2) how to use session information for more informative search.

Jiliang Tang, Michigan State University

Toward Automated Deep Recommender Systems

Deep learning has injected new life into recommender systems and deep recommender systems (DRS) have achieved very promising performance in various recommendation scenarios. Compared to traditional recommendation methods, DRSs often have complicated architectures in order to embed complex representations of users/items and simulate nonlinear user-item interaction pattern. To build a deep model for a given recommendation task, we often need to design its architecture in a hand-crafted manner. However, the design space for these layers can be huge. Therefore, automatically designing these layers is highly desired. In this talk, I will discuss how to automatically determine the embedding dimension and discover the optimal loss function for DRSs.

Musen Wen, Walmart Ads

Computational Advertising in e-Commerce: an Industry Review and New Challenge

Online advertising, both displayed ads and sponsored search, have been powering the internet business in the past two decades. In recent years, advertising has become the new fly-wheel for eCommerce platforms. It provides an effective marketing channel for advertisers, generating sizable revenue for the platform and providing an enriched experience for shoppers. This talk will review the commonality and differences in the marketplace platforms. We will also discuss the challenge and open problems from the view of modern computational advertising and future directions.

Yongfeng Zhang, Rutgers University

Towards Trustworthy Recommender Systems: From Shallow Models to Deep Models to Large Models

As the bridge between humans and AI, recommender system is at the frontier of Human-centered AI research. However, inappropriate use or development of recommendation techniques may bring negative effects to humans and the society at large, such as user distrust due to the non-transparency of the recommendation mechanism, unfairness of the recommendation algorithm, user uncontrollability of the recommendation system, as well as user privacy risks due to the extensive use of users’ private data for personalization. In this talk, we will discuss how to build trustworthy recommender systems along the progress that recommendation algorithms advance from shallow models to deep models to large models, including but not limited to the unique role of recommender system research in the AI community as a representative Subjective AI task, the relationship between Subjective AI and trustworthy computing, as well as typical recommendation methods on different perspectives of trustworthy computing, such as causal and counterfactual reasoning, neural-symbolic modeling, natural language explanations, federated learning, user controllable recommendation, echo chamber mitigation, personalized prompt learning, and beyond.