Hua Chu

Xiangming Li, Hua Chu, Chengyu Feng et al.

Distilling accurate user preferences from noisy implicit feedback remains a fundamental bottleneck in recommendation systems, highlighting the need for recommendation denoising. However, real-world data lack explicit noise annotations, forcing existing methods to rely on unsupervised side information or handcrafted heuristics. These approaches often incur high external costs, generalize poorly, or depend on unreliable priors, causing noise misidentification and corrupting true user preference representations. To address these limitations, we propose a paradigm-level reformulation of recommendation denoising. Instead of indirectly inferring noisy interactions through heuristics, our Creation-Recognition paradigm proactively creates labeled noisy interactions and trains a dedicated recognizer to identify them, transforming denoising from heuristic filtering into supervised learning. Based on this paradigm, we present ANCHOR, an agent-based framework inspired by recent LLM-as-User research. ANCHOR simulates user behaviors to generate realistic noise labels and enables supervised denoising through two stages: noise creation and noise recognition. In the noise creation stage, ANCHOR adopts a recommender-in-the-loop agentic architecture to synthesize both diverse out-of-preference noise and informative boundary-adjacent noise. For out-of-preference noise, it implements five extensible simulation mechanisms to approximate major sources of noisy implicit feedback. For boundary-adjacent noise, an adversarial boundary refinement mechanism generates ambiguous interactions that challenge the recognizer and target the decision boundary. In the noise recognition stage, ANCHOR leverages the generated labels to train a reusable parametric recognizer that integrates collaborative signals and semantic representations to detect noise patterns in real interaction data.

Yangtao Zhou, Wenhao You, Hua Chu et al.

Group Recommendation (GR) aims to suggest items to a group of users, which has become a critical component of modern social platforms. Existing GR methods focus on aggregating individual user preferences with advanced neural networks to infer group preferences. Despite effectiveness, they essentially treat group preference learning as a simple preference aggregation process, failing to capture the complex dynamics of real-world group decision-making. To address these limitations, we propose AgentGR, a novel Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendations, inspired by the semantic reasoning and human behavior simulation capabilities of LLM-driven agents. It aims to jointly capture collaborative-semantic user preferences for member-role-playing and simulate dynamic group interactions to reflect real-world group decision-making processes, thereby boosting recommendation performance. Specifically, to capture collaborative-semantic user preferences, we introduce a semantic meta-path guided chain-of-preference reasoning mechanism that integrates high-order collaborative filtering signals and textual semantics to improve user preference profiles. To model the complex dynamics of group decision-making, we first recognize group topic and leadership to explicitly model the influencing factors within the group decision processes. Building on these, we simulate group-level decision dynamics via two multi-agent simulation strategies for recommendations: a static workflow-based strategy for efficiency and a dynamic dialogue-based strategy for precision. Extensive experiments on two real-world datasets show that AgentGR significantly outperforms state-of-the-art baselines in both recommendation accuracy and group decision simulation, highlighting its potential for real-world GR applications.