Abstract: Recommender systems typically collect and analyze user data, which raises the risk of privacy invasion. User-sensitive information can be leaked from the user portrait, e.g., user embedding, ...

There once was a time where going viral on the internet actually meant something. Long ago, in the early 2010s, 500,000 views could actually land you on daytime TV, where you could experience the ...

Architected a polyglot e-commerce platform integrating a React/RTK frontend, a Node.js backend, and a Python ML microservice. The platform's core is a hybrid, real-time recommendation system using ...

Yandex has introduced ARGUS (AutoRegressive Generative User Sequential modeling), a large-scale transformer-based framework for recommender systems that scales up to one billion parameters. This ...

In this tutorial, we explore how to build an intelligent and self-correcting question-answering system using the DSPy framework, integrated with Google’s Gemini 1.5 Flash model. We begin by defining ...

Background People increasingly rely on online health information for their health-related decision-making. Given the overwhelming amount of information available, the risk of misinformation is high.

Download PDF Join the Discussion View in the ACM Digital Library Figure 1. An example of interaction between a Travel Agent and a user. The agent can serve as an information carrier for travel-related ...

Recommender systems have become indispensable in the information age, guiding users through vast datasets and enabling personalized, contextually relevant interactions. By leveraging user and item ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...