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Fangyuan Luo

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach

Jundong Chen
Honglei Zhang
Chunxu Zhang
Fangyuan Luo
Yidong Li

Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personalization, leading to suboptimal performance, i.e., the aggregation bottleneck. This issue stems from the inherent heterogeneity across numerous clients in FR, which drives the global model to deviate from local optima. To this end, we propose FedEM, which elastically merges the global and local models to compensate for impaired personalization. Unlike existing personalized federated recommendation (pFR) methods, FedEM (1) investigates the aggregation bottleneck in FR through theoretical insights, rather than relying on heuristic analysis; (2) leverages off-the-shelf local models rather than designing additional mechanisms to boost personalization. Extensive experiments demonstrate that our method preserves client personalization during collaborative training, outperforming state-of-the-art baselines.

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AAAI Conference 2025 Short Paper

Alleviating Dual Biases in Recommendation (Student Abstract)

Sijin Lu
Fangyuan Luo
Jun Wu

Causal Inference (CI) plays a crucial role in building unbiased recommender systems. However, most current CI-based debiasing methods only pay attention on either popularity bias or conformity bias. This paper presents a Disentangled Counterfactual Reasoning framework to alleviate dual biases in recommendation, so called DCR. Concretely, we consider the impact of both item popularity and user conformity during training, and separate their indirect effects by disentangling user and item embeddings into biased and unbiased components. In the inference stage, we perform counterfactual reasoning to simultaneously mitigate the indirect and direct effects of bias factors. Experimental results demonstrate the effectiveness of our DCR.

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AAAI Conference 2024 Short Paper

Optimizing Recall in Deep Graph Hashing Framework for Item Retrieval (Student Abstract)

Fangyuan Luo
Jun Wu

Hashing-based recommendation (HR) methods, whose core idea is mapping users and items into hamming space, are common practice to improve item retrieval efficiency. However, existing HR fails to align optimization objective (i.e., Bayesian Personalized Ranking) and evaluation metric (i.e., Recall), leading to suboptimal performance. In this paper, we propose a smooth recall loss (termed as SRLoss), which targets Recall as the optimization objective. Due to the existence of discrete constraints, the optimization problem is NP-hard. To this end, we propose an approximation-adjustable gradient estimator to solve our problem. Experimental Results demonstrate the effectiveness of our proposed method.

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IJCAI Conference 2022 Conference Paper

Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback

Fangyuan Luo
Jun Wu
Tao Wang

We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pairwise loss) with the benchmark metrics for ranking quality (e. g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn binary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation.

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AAAI Conference 2021 Short Paper

Semi-Discrete Social Recommendation (Student Abstract)

Fangyuan Luo
Jun Wu
Haishuai Wang

Combining matrix factorization (MF) with network embedding (NE) has been a promising solution to social recommender systems. However, such a scheme suffers from the online predictive efficiency issue due to the ever-growing users and items. In this paper, we propose a novel hashingbased social recommendation model, called semi-discrete socially embedded matrix factorization (S2MF), which leverages the dual advantages of social information for recommendation effectiveness and hashing trick for online predictive efficiency. Experimental results demonstrate the advantages of S2MF over state-of-the-art discrete recommendation models and its real-valued competitors.

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IS Journal 2020 Journal Article

Semi-discrete Matrix Factorization

Jun Wu
Fangyuan Luo
Yujia Zhang
Haishuai Wang

Discrete matrix factorization (DMF) has been a promising solution to improve the inferring efficiency of matrix factorization (MF) against the rapidly growing numbers of users and items. However, DMF suffers from a serious encoding loss due to its oversimplified modeling on the original data geometry. In this article, we propose a semi-discrete matrix factorization (SDMF) model to combine the predicting efficacy of MF with the inferring efficiency of DMF. It first learns real-valued latent features by MF, and then, taking them as group-wise and point-wise smoothness, learns binary codes in the DMF framework, for preserving the geometrical structures collectively hidden in users and items, as well as aligning binary codes originated from Hamming space with their real-valued counterparts learned from vector space. Particularly, we devise a computationally efficient optimization algorithm to estimate model parameters. Extensive evaluations on three real-world datasets clearly demonstrate the superiority of our SDMF model over state-of-the-art hash-based recommendation methods.

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