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Xiaoming Shi

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11 papers

2 author rows

AAAI Conference 2025 Conference Paper

Coherency Improved Explainable Recommendation via Large Language Model

Shijie Liu
Ruixin Ding
Weihai Lu
Jun Wang
Mo Yu
Xiaoming Shi
Wei Zhang

Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rating prediction and explanation generation in a multi-task manner. However, these works suffer from incoherence between predicted ratings and explanations. To address the issue, we propose a novel framework that employs a large language model (LLM) to generate a rating, transforms it into a rating vector, and finally generates an explanation based on the rating vector and user-item information. Moreover, we propose utilizing publicly available LLMs and pre-trained sentiment analysis models to automatically evaluate the coherence without human annotations. Extensive experimental results on three datasets of explainable recommendation show that the proposed framework is effective, outperforming state-of-the-art baselines with improvements of 7.3% in explainability and 4.4% in text quality.

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AAAI Conference 2025 Conference Paper

STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling

Jieyi Wang
Yue Huang
Zeming Liu
Dexuan Xu
Chuan Wang
Xiaoming Shi
Ruiyuan Guan
Hongxing Wang

Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus on basic empathetic dialogue or QA with minimal professional knowledge and without goal guidance. In many real-world counseling scenarios, clients often seek multi-type help, such as diagnosis, consultation, therapy, console, and common questions, but existing dialogue systems struggle to combine different dialogue types naturally. In this paper, we identify this challenge as how to construct mixed-type dialogue systems for psychological counseling that enable clients to clarify their goals before proceeding with counseling. To mitigate the challenge, we collect a mixed-type counseling dialogues corpus termed STAMPsy, covering five dialogue types, task-oriented dialogue for diagnosis, knowledge-grounded dialogue, conversational recommendation, empathetic dialogue, and question answering, over 5,000 conversations. Moreover, spatiotemporal-aware knowledge enables systems to have world awareness and has been proven to affect one's mental health. Therefore, we link dialogues in STAMPsy to spatiotemporal state and propose a spatiotemporal-aware mixed-type psychological counseling dataset. Additionally, we build baselines on STAMPsy and develop an iterative self-feedback psychological dialogue generation framework, named Self-STAMPsy. Results indicate that clarifying dialogue goals in advance and utilizing spatiotemporal states are effective.

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ICLR Conference 2025 Conference Paper

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Xiaoming Shi
Shiyu Wang
Yuqi Nie
Dianqi Li
Zhou Ye 0001
Qingsong Wen
Ming Jin

Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility. Code is available at https://github.com/Time-MoE/Time-MoE

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AAAI Conference 2023 Conference Paper

Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes

Chao Qu
Xiaoyu Tan
Siqiao Xue
Xiaoming Shi
James Zhang
Hongyuan Mei

We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in reinforcement learning. To this end, we present a novel framework of the model-based reinforcement learning where the agent's actions and observations are asynchronous stochastic discrete events occurring in continuous-time. We model the dynamics of the environment by Hawkes process with external intervention control term and develop an algorithm to embed such process in the Bellman equation which guides the direction of the value gradient. We demonstrate the superiority of our method in both synthetic simulator and real-data experiments.

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IJCAI Conference 2023 Conference Paper

Full Scaling Automation for Sustainable Development of Green Data Centers

Shiyu Wang
Yinbo Sun
Xiaoming Shi
Zhu Shiyi
Lin-Tao Ma
James Zhang
Yangfei Zheng
Liu Jian

The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate. An important focus of this effort is to improve resource utilization in order to save electricity usage. Our proposed Full Scaling Automation (FSA) mechanism is an effective method of dynamically adapting resources to accommodate changing workloads in large-scale cloud computing clusters, enabling the clusters in data centers to maintain their desired CPU utilization target and thus improve energy efficiency. FSA harnesses the power of deep representation learning to accurately predict the future workload of each service and automatically stabilize the corresponding target CPU usage level, unlike the previous autoscaling methods, such as Autopilot or FIRM, that need to adjust computing resources with statistical models and expert knowledge. Our approach achieves significant performance improvement compared to the existing work in real-world datasets. We also deployed FSA on large-scale cloud computing clusters in industrial data centers, and according to the certification of the China Environmental United Certification Center (CEC), a reduction of 947 tons of carbon dioxide, equivalent to a saving of 1538, 000 kWh of electricity, was achieved during the Double 11 shopping festival of 2022, marking a critical step for our company’s strategic goal towards carbon neutrality by 2030.

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NeurIPS Conference 2023 Conference Paper

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Xiaoming Shi
Siqiao Xue
Kangrui Wang
Fan Zhou
James Zhang
Jun Zhou
Chenhao Tan
Hongyuan Mei

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a large language model in event prediction. Particularly, the language model performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the language model learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework---thanks to the reasoning capabilities of large language models---could significantly outperform the state-of-the-art event sequence models.

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AAAI Conference 2023 Conference Paper

Learning Semantic Alignment with Global Modality Reconstruction for Video-Language Pre-training towards Retrieval

Mingchao Li
Xiaoming Shi
Haitao Leng
Wei Zhou
Hai-Tao Zheng
Kuncai Zhang

Video-language pre-training for text-based video retrieval tasks is vitally important. Previous pre-training methods suffer from the semantic misalignments. The reason is that these methods ignore sequence alignments but focusing on critical token alignment. To alleviate the problem, we propose a video-language pre-training framework, termed videolanguage pre-training For lEarning sEmantic aLignments (FEEL), to learn semantic alignments at the sequence level. Specifically, the global modality reconstruction and the cross- modal self-contrasting method is utilized to learn the alignments at the sequence level better. Extensive experimental results demonstrate the effectiveness of FEEL on text-based video retrieval and text-based video corpus moment retrieval.

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NeurIPS Conference 2023 Conference Paper

Prompt-augmented Temporal Point Process for Streaming Event Sequence

Siqiao Xue
Yan Wang
Zhixuan Chu
Xiaoming Shi
Caigao JIANG
Hongyan Hao
Gangwei Jiang
Xiaoyun Feng

Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real world applications, the event data typically comes in a streaming manner, where the distribution of the patterns may shift over time. Under the privacy and memory constraints commonly seen in real scenarios, how to continuously monitor a TPP to learn the streaming event sequence is an important yet under-investigated problem. In this work, we approach this problem by adopting Continual Learning (CL), which aims to enable a model to continuously learn a sequence of tasks without catastrophic forgetting. While CL for event sequence is less well studied, we present a simple yet effective framework, PromptTPP, by integrating the base TPP with a continuous-time retrieval prompt pool. In our proposed framework, prompts are small learnable parameters, maintained in a memory space and jointly optimized with the base TPP so that the model is properly instructed to learn event streams arriving sequentially without buffering past examples or task-specific attributes. We formalize a novel and realistic experimental setup for modeling event streams, where PromptTPP consistently sets state-of-the-art performance across two real user behavior datasets.

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NeurIPS Conference 2022 Conference Paper

HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

Siqiao Xue
Xiaoming Shi
James Zhang
Hongyuan Mei

In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure. We propose HYPRO, a hybridly normalized probabilistic model that naturally fits this task: its first part is an autoregressive base model that learns to propose predictions; its second part is an energy function that learns to reweight the proposals such that more realistic predictions end up with higher probabilities. We also propose efficient training and inference algorithms for this model. Experiments on multiple real-world datasets demonstrate that our proposed HYPRO model can significantly outperform previous models at making long-horizon predictions of future events. We also conduct a range of ablation studies to investigate the effectiveness of each component of our proposed methods.

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JBHI Journal 2022 Journal Article

Understanding Patient Query With Weak Supervision From Doctor Response

Xiaoming Shi
Sendong Zhao
Yuxuan Wang
Xi Chen
Ziheng Zhang
Yefeng Zheng
Wanxiang Che

Currently, the need for high-quality dialogue systems that assist users to conduct self-diagnosis is rapidly increasing. Slot filling for automatic diagnosis, which converts medical queries into structured representations, plays an important role in diagnostic dialogue systems. However, the lack of high-quality datasets limits the performance of slot filling. While medical communities like AskAPatient usually have multiple rounds of diagnostic dialogue containing colloquial input and professional responses from doctors. Therefore, the data of diagnostic dialogue in medical communities can be utilized to solve the main challenges in slot filling. This paper proposes a two-step training framework to make full use of these unlabeled dialogue data in medical communities. To promote further researches, we provide a Chinese dataset with 2, 652 annotated samples and a large amount of unlabeled samples. Experimental results on the dataset demonstrate the effectiveness of the proposed method with an increase of 6. 32% in Micro F1 and 8. 20% in Macro F1 on average over strong baselines.

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AAAI Conference 2020 Conference Paper

Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses

Xiaoming Shi
Haifeng Hu
Wanxiang Che
Zhongqian Sun
Ting Liu
Junzhou Huang

In this work, we consider the medical slot ﬁlling problem, i. e. , the problem of converting medical queries into structured representations which is a challenging task. We analyze the effectiveness of two points: scattered keywords in user utterances and weak supervision with responses. We approach the medical slot ﬁlling as a multi-label classiﬁcation problem with label-embedding attentive model to pay more attention to scattered medical keywords and learn the classiﬁcation models by weak-supervision from responses. To evaluate the approaches, we annotate a medical slot ﬁlling data and collect a large scale unlabeled data. The experiments demonstrate that these two points are promising to improve the task.

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