Recently, large language models (LLMs) have shown great promise in sequential recommendation. Existing methods typically transform users' historical interactions into textual sequences and then feed these sequences into LLMs to generate recommended items. However, since the output of LLMs is limited to recommendations, the models must analyze user interactions and infer user preferences implicitly. This implicit approach constrains the expressive reasoning abilities of LLMs. Moreover, as the output of LLMs consists solely of recommended items, the resulting recommendations lack explainability. To address these issues, we propose RE2, which enables LLMs to explicitly generate reasoning content before providing recommendations. Making the reasoning process explicit helps elicit the reasoning abilities of LLMs and simultaneously enhances the explainability of recommendation results. RE2 consists of three steps: (1) Reasoning Collection, which collects reasoning data by guiding LLMs to generate both reasoning content and recommended items for each interaction sequence. (2) Pattern Imitation, which leverages the collected data to train LLMs via supervised fine-tuning to imitate the pattern of first generating reasoning content and then providing recommendations. (3) Pattern Internalization, which further internalizes this reasoning-and-recommendation pattern and enhances both recommendation performance and the rationality of the reasoning content through reinforcement learning. RE2 can be implemented under both the self-distillation and teacher-distillation frameworks, without requiring external user metadata such as user reviews. Experimental results demonstrate the effectiveness of RE2 in improving recommendation performance and in generating high-quality reasoning content. Furthermore, we show that RE2 can mitigate popularity bias while maintaining recommendation accuracy to some extent. Our data and code are available at https://github.com/zhiyuanc2001/RE2.

Simulating high quality user behavior data has always been a fundamental yet challenging problem in human-centered applications such as recommendation systems, social networks, among many others. The major difficulty of user behavior simulation originates from the intricate mechanism of human cognitive and decision processes. Recently, substantial evidence has suggested that by learning huge amounts of web knowledge, large language models (LLMs) can achieve human-like intelligence and generalization capabilities. Inspired by such capabilities, in this article, we take an initial step to study the potential of using LLMs for user behavior simulation in the recommendation domain. To make LLMs act like humans, we design profile, memory and action modules to equip them, building LLM-based agents to simulate real users. To enable interactions between different agents and observe their behavior patterns, we design a sandbox environment, where each agent can interact with the recommendation system, and different agents can converse with their friends via one-to-one chatting or one-to-many social broadcasting. In the experiments, we first demonstrate the believability of the agent-generated behaviors based on both subjective and objective evaluations. Then, to show the potential applications of our method, we simulate and study two social phenomena including (1) information cocoons and (2) user conformity behaviors. We find that controlling the personalization degree of recommendation algorithms and improving the heterogeneity of user social relations can be two effective strategies for alleviating the problem of information cocoon, and the conformity behaviors can be highly influenced by the amount of user social relations. To advance this direction, we have released our project at https://github.com/RUC-GSAI/YuLan-Rec .

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various information retrieval fields, such as search engines, social networks, recommender systems, and conversational assistants. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way of information retrieval.

Since the advent of large language models (LLMs), the field of tool learning has remained very active in solving various tasks in practice, including but not limited to information retrieval. This half-day tutorial provides basic concepts of this field and an overview of recent advancements with several applications. In specific, we start with some foundational components and architecture of tool learning (i.e., cognitive tool and physical tool), and then we categorize existing studies in this field into tool-augmented learning and tool-oriented learning, and introduce various learning methods to empower LLMs this kind of capability. Furthermore, we provide several cases about when, what, and how to use tools in different applications. We end with some open challenges and several potential research directions for future studies. We believe this tutorial is suited for both researchers at different stages (introductory, intermediate, and advanced) and industry practitioners who are interested in LLMs and tool learning.

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning. Recently, Large Language Models (LLMs) have demonstrated remarked potential in simulating human-level intelligence and have been used in building autonomous agents for various tasks. However, the potential of using LLMs in simulating search behaviors has not yet been fully explored. In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. The proposed simulator can simulate users' querying, clicking, and stopping behaviors during search, and thus, is capable of generating complete search sessions for specific search tasks. Empirical investigation on a real user behavior dataset shows that the proposed simulator outperforms existing methods in query generation and is comparable to traditional methods in predicting user clicks and stopping behaviors. These results not only validate the effectiveness of using LLMs for user simulation but also shed light on the development of a more robust and generic user simulators.

The prosperity of massive open online courses provides fodder for plentiful research efforts on adaptive learning. However, current open-access educational datasets are still far from sufficient to meet the need for various topics of adaptive learning. Existing released datasets often cover only small-scale data, lack fine-grained knowledge concepts. They are even difficult to curate and supplement due to platform limitations. In this work, we construct MOOCCubeX, a large, knowledge-centered repository consisting of 4,216 courses, 230,263 videos, 358,265 exercises, 637,572 fine-grained concepts and over 296 million behavioral data of 3,330,294 students, for supporting the research topics on adaptive learning in MOOCs. Licensed by XuetangX, one of the largest MOOC websites in China, we obtain abundant and diverse course resources and student behavioral data and are permitted to make subsequent periodic updates. We propose a framework to accomplish data processing, weakly supervised fine-grained concept graph mining, and data curation to improve usability and richness. Based on the fine-grained concepts, we re-organize the data from the knowledge perspective and acquire more external learning resources from the web. Our repository is now available at https://github.com/THU-KEG/MOOCCubeX.