This tutorial covers topics on multi-agentic recommender systems — recommender systems augmented with Large Language Models (LLMs) and multi-agent orchestration to enable multi-step reasoning, tool use, and interactive decision-making. The tutorial emphasizes foundational concepts, reusable design patterns, and practical lessons learned from large-scale e-commerce deployments. Specifically, we first cover background and recent trends in generative recommender systems and their connection to agentic approaches. We then survey major deployment areas in industry and review the agent orchestration frameworks developed to support them. Finally, we present a project walkthrough that traces the full lifecycle of an agentic recommender system, from scoping and data definition through modeling, deployment, and monitoring, to provide actionable deployment insights. The tutorial bridges perspectives from information retrieval (IR), recommender systems (RecSys), and large-scale industrial practice. The accompanying material can be found at agenticrecsys.github.io.

Keyword extraction is one of the core tasks in natural language processing. Classic extraction models are notorious for having a short attention span which make it hard for them to conclude relational connections among the words and sentences that are far from each other. This, in turn, makes their usage prohibitive for generating keywords that are inferred from the context of the whole text. In this paper, we explore using Large Language Models (LLMs) in generating keywords for items that are inferred from the items’ textual metadata. Our modeling framework includes several stages to fine grain the results by avoiding outputting keywords that are non-informative or sensitive and reduce hallucinations common in LLM’s. We call our LLM-based framework Theme-Aware Keyword Extraction (LLM-TAKE). We propose two variations of framework for generating extractive and abstractive themes for products in an E-commerce setting. We perform an extensive set of experiments on three real data sets and show that our modeling framework can enhance accuracy-based and diversity-based metrics when compared with benchmark models.

In e-Commerce Industry, customers’ purchase decision of an item are usually influenced by the reference price of that item, which is implied within the context of the items (e.g. prices of an item set from search/recommendation) or external environments (e.g. prices from another e-Commerce platform). Despite of the prevalence and influence of the reference price on customers’ behavior, existing works in Information Retrieval domain do not exploit the value of the reference price in ranking problems. In this paper, we propose a list-wise ranking model named "Prospect-Net" by incorporating the prospect theory, which is the theoretical foundation for framing the reference price. We consider the Top-K retrieval task under a product recommendation setting, and demonstrate the effectiveness of Prospect-Net to capture various forms of reference price under different scenarios. Polynomial solutions are proposed to solve the Top-K retrieval problem for some of the cases where the reference price is dependent on the recommended set of items to the user. Both offline e valuation and online experiments are performed on a real-world industrial dataset with significant performance improvement.