Qihao Shi

AAAI Conference 2025 Conference Paper

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution

• Shengjia Zhang
• Jiawei Chen
• Changdong Li
• Sheng Zhou
• Qihao Shi
• Yan Feng
• Chun Chen
• Can Wang

Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths --- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to distributional shifts; 2) Respective limitations --- stemming from their use of different distribution distance metrics in DRO optimization, SL exhibits high sensitivity to false negative instances, whereas CCL suffers from low data utilization. To address these limitations, this work proposes a new loss function, DrRL, which generalizes SL and CCL by leveraging Rényi-divergence in DRO optimization. DrRL incorporates the advantageous structures of both SL and CCL, and can be demonstrated to effectively mitigate their limitations. Extensive experiments have been conducted to validate the superiority of DrRL on both recommendation accuracy and robustness.

AAAI Conference 2023 Conference Paper

Robust Sequence Networked Submodular Maximization

• Qihao Shi
• Bingyang Fu
• Can Wang
• Jiawei Chen
• Sheng Zhou
• Yan Feng
• Chun Chen

In this paper, we study the Robust optimization for sequence Networked submodular maximization (RoseNets) problem. We interweave the robust optimization with the sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable and calls for new robust algorithms. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithms, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.

TCS Journal 2021 Journal Article

Profit maximization for competitive social advertising

• Qihao Shi
• Can Wang
• Deshi Ye
• Jiawei Chen
• Sheng Zhou
• Yan Feng
• Chun Chen
• Yanhao Huang

AAAI Conference 2020 Conference Paper

DGE: Deep Generative Network Embedding Based on Commonality and Individuality

• Sheng Zhou
• Xin Wang
• Jiajun Bu
• Martin Ester
• Pinggang Yu
• Jiawei Chen
• Qihao Shi
• Can Wang

Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.

AAAI Conference 2020 Conference Paper

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

• Jiawei Chen
• Can Wang
• Sheng Zhou
• Qihao Shi
• Jingbang Chen
• Yan Feng
• Chun Chen

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user’s preference; or adaptively infer personalized con- fidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batchbased learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on realworld datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.