Multi-Agent Collaboration Framework for Recommender Systems

LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our framework, recommendation tasks are addressed through the collaborative efforts of various specialized agents, including Manager, User/Item Analyst, Reflector, Searcher, and Task Interpreter, with different working flows. Furthermore, we provide application examples of how developers can easily use MACRec on various recommendation tasks, including rating prediction, sequential recommendation, conversational recommendation, and explanation generation of recommendation results. The framework and demonstration video are publicly available at https://github.com/wzf2000/MACRec.

EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems

Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly recognized for their ability to improve long-term user engagement. Yet, the field grapples with challenges such as the absence of accessible frameworks, inconsistent evaluation standards, and the complexity of replicating prior work. Addressing these obstacles, we present EasyRL4Rec, a user-friendly and efficient library tailored for RL-based RSs. EasyRL4Rec features lightweight, diverse RL environments built on five widely-used public datasets, and is equipped with comprehensive core modules that offer rich options to ease the development of models. It establishes consistent evaluation criteria with a focus on long-term impacts and introduces customized solutions for state modeling and action representation tailored to recommender systems. Additionally, we share valuable insights gained from extensive experiments with current methods. EasyRL4Rec aims to facilitate the model development and experimental process in the domain of RL-based RSs. The library is openly accessible at https://github.com/chongminggao/EasyRL4Rec.

Towards Representation Alignment and Uniformity in Collaborative Filtering

Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representation space, and the Bayesian personalized ranking (BPR) loss is usually adopted as the objective function to learn informative encoders. Existing studies mainly focus on designing more powerful encoders (e.g., graph neural network) to learn better representations. However, few efforts have been devoted to investigating the desired properties of representations in CF, which is important to understand the rationale of existing CF methods and design new learning objectives. In this paper, we measure the representation quality in CF from the perspective of alignment and uniformity on the hypersphere. We first theoretically reveal the connection between the BPR loss and these two properties. Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance. Based on the analyses results, a learning objective that directly optimizes these two properties is proposed, named DirectAU. We conduct extensive experiments on three public datasets, and the proposed learning framework with a simple matrix factorization model leads to significant performance improvements compared to state-of-the-art CF methods. Our implementations are publicly available at https://github.com/THUwangcy/DirectAU.