509496 - INFORMATION RETRIEVAL AND RECOMMENDER SYSTEMS

The aim of the course is to provide an introduction to the fundamental concepts, models and techniques related to Information Retrieval Systems (aka Search Engines) and to Recommender Systems. These two categories of systems are nowadays largely diffused, and they offer an automatic support for the access to information potentially useful (relevant) to specific users’ needs. While search Engines require users to explicitly express their information needs by formulating a query (pull technology), Recommender Systems do not require an explicit users’ actions, as they provide users with information/services of potential relevance to them, based on user profiles (push technology). The course also showcase how Large Language Models (LLMs) are integrated into IR and RS pipelines, including their use in Retrieval-Augmented Generation (RAG) systems. After successfully completing the course, students will be able to: - Understand the basic structure of search engines and recommender systems - Know the basic models at the basis of both categories of systems - Describe the main challenges behind these technologies - Recognize how modern systems (e.g., LLMs, RAG) support retrieval and recommendation - Compare IR, RS, and conversational AI systems in terms of functionality and user interaction