i A gent: LLM Agent as a Shield between User and Recommender Systems (2025)

Abstract: Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform’s recommendation algorithms. However, the defect of recommendation algorithms may put users in very vulnerable positions under this paradigm. First, many sophisticated models are often designed with commercial objectives in mind, focusing on the platform’s benefits, which may hinder their ability to protect and capture users’ true interests. Second, these models are typically optimized using data from all users, which may overlook individual user’s preferences. Due to these shortcomings, users may experience several disadvantages under the traditional user-platform direct exposure paradigm, such as lack of control over the recommender system, potential manipulation by the platform, echo chamber effects, or lack of personalization for less active users due to the dominance of active users during collaborative learning. Therefore, there is an urgent need to develop a new paradigm to protect user interests and alleviate these issues. Recently, some researchers have introduced LLM agents to simulate user behaviors, these approaches primarily aim to optimize platform-side performance, leaving core issues in recommender systems unresolved. To address these limitations, we propose a new user-agent-platform paradigm, where agent serves as the protective shield between user and recommender system that enables indirect exposure. To this end, we first construct four recommendation datasets, denoted as InstructRec, along with user instructions for each record. To understand user’s intention, we design an Instruction-aware Agent capable of using tools to acquire knowledge from external environments. Moreover, we introduce an Individual Instruction-aware Agent, which incorporates a dynamic memory mechanism to optimize from individual feedback. Results on four datasets demonstrate that consistently achieves an average improvement of 16.6% over SOTA baselines across ranking metrics. Moreover, iAgent mitigates echo chamber effects and effectively alleviates the model bias in disadvantaged users (less-active), serving as a shield between user and recommender systems.

## introduction over the past decades, recommender systems have been extensively applied across various platforms to provide personalized services to users. in the traditional ecosystem of recommender systems, the recommendation models are predominantly delivered through a user-platform paradigm, where users are directly subject to the platform's algorithms. this paradigm places users in a vulnerable position, such as lack of control over their recommendation results, potentially being manipulated by the platform's recommendation algorithms, being trapped in echo chambers, or lack of personalization for those less active users due to the active users' dominance of the recommendation algorithm. firstly, the majority of recommendation models @xcite @xcite are designed to optimize the commercial objectives of the platforms, such as increasing user clicks or conversion rates in ecommerce. this often results in users losing sight of their actual needs due to the algorithmic manipulation @xcite @xcite . secondly, although recommendation models aims at offering personalized services, they are primarily optimized based on data from all users, paying insufficient attention to individual preferences and unique interests @xcite @xcite . as a consequence of these shortcomings, users often fall into the echo chamber effects @xcite ranking list feedback feedback (b) figure 1 : (a) previous recommendation ecosystem primarily focused on designing sophisticated models to enhance the ranking performance so as to increase platform's benefit. however, they overlooked the user's proactive instructions and put users under the direct control of recommender systems. (b) in contrast, we build an individual instruction-aware agent for each user, which generates re-ranking results based on the user's active instructions. the agent's memory component is influenced solely by the individual user, providing an individualized personal service. 2020; @xcite , where algorithms reinforce user's existing interests or beliefs through repeated recommendation of homogeneous items, leading to a lack of diversity in recommended contents. furthermore, the models tend to be biased towards advantaged (active) users, neglecting the interests of disadvantaged (less-active) users, resulting in a lack of personalization for some users. to tackle these issues, researchers have approached the problem from various perspectives. on one hand, efforts are made to better understand user interests, such as using user's explicit feedback to improve the model performance and explanation @xcite or allowing users to better express their needs through conversational recommender sysetms (crs) @xcite . on the other hand, comprehensive models are developed to infer user interests from various dimensions, such as capturing user's diverse interests based on multi-behavior and multi-interest modeling @xcite @xcite . most recently, languagebased agents are utilized to mock the user behaviors and explore the user interests @xcite . however, the two challenges remain insufficiently addressed due to the reliance on modeling user interests across all users' data and the focus on platform-side optimization. to address these limitations, we propose a new user-agent-platform paradigm, where agent serves as the protective shield between user and recommender system that enables indirect exposure. our contributions are three-fold: ## methodology in this part, we firstly introduce the naive solution iagent based on instructrec, which can learn the intention from the user instruction. next, we introduce our i 2 agent equipped with individual dynamic memory. the workflow of models are shown in fig. 2 . all the prompt templates used in iagent and i 2 agent and examples of responses are provided in appendix d. ## conclusion in this work, we first design a straightforward instruction-aware agent (iagent) to analyze user instructions and integrate relevant and comprehensive knowledge. moreover, to enhance the agent's personalized abilities, we propose individual instruction-aware agent (i 2 agent), which incorporates a dynamic memory mechanism to learn from user's personal feedback and extracts the dynamic interests. in addition to these technical contributions, our work also presents unique and complementary avenues for future research. we discuss potential future directions and open challenges in appendix e.