Tutorials

    With the emergence of various information access systems exhibiting increasing complexity, there is a critical need for sound and scalable means of automatic evaluation. To address this challenge, user simulation emerges as a promising solution. This half-day tutorial focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We systematically review major research progress, covering both general frameworks for designing user simulators, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. We also highlight some important future research directions.

    Conversational systems are designed to offer human users social support or functional services through natural language interactions. Typical conversation researches mainly focus on the response-ability of the system, such as dialogue context understanding and response generation. In the era of large language models (LLMs), LLM-augmented conversational systems showcase exceptional capabilities of responding to user queries for different language tasks. However, as LLMs are trained to follow users' instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent's awareness of goals in a wide range of conversational systems, including proactive, non-collaborative, and multi-goal conversational systems. In addition, we will discuss the main open challenges in developing agent’s goal awareness in LLM-augmented conversational systems and several potential research directions for future studies.

    Generative retrieval (GR) has become a highly active area of information retrieval (IR) that has witnessed significant growth recently. Compared to the traditional ``index-retrieve-then-rank'' pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications.
   We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and the applications of GR. We end by outlining remaining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.

    The powerful large language models (LLMs) have played a pivotal role in advancing recommender systems. Recently, in both academia and industry, there has been a surge of interest in developing LLMs for recommendation, referred to as LLM4Rec. This includes endeavors like leveraging LLMs for generative item retrieval and ranking, as well as the exciting possibility of building universal LLMs for diverse open-ended recommendation tasks. These developments hold the potential to reshape the traditional recommender paradigm, paving the way for the next-generation recommender systems.
    In this tutorial, we aim to retrospect the evolution of LLM4Rec and conduct a comprehensive review of existing research. In particular, we will clarify how recommender systems benefit from LLMs through a variety of perspectives, including the model architecture, learning paradigm, and the strong abilities of LLMs such as chatting, generalization, planning, and generation.
    Furthermore, we will discuss the critical challenges and open problems in this emerging field, for instance, the trustworthiness, efficiency, and model retraining issues.
    Lastly, we will summarize the implications of previous work and outline future research directions.
    We believe that this tutorial will assist the audience in better understanding the progress and prospects of LLM4Rec, inspiring them for future exploration. This, in turn, will drive the prosperity of LLM4Rec, possibly fostering a paradigm shift in recommendation systems.