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Xiaochun Yang

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14 papers

1 author row

AAAI Conference 2026 Conference Paper

SEFEL: A Simple Yet Effective Framework for Fast Event Linking

Yinan Liu
Ziyang Zhang
Bin Wang
Xiaochun Yang

Event linking aims to associate event mentions in text with their corresponding entries in a knowledge base (KB). This task can help text understanding to benefit downstream tasks (e.g., question answering) and expand the KB through new event knowledge mentioned in the text. Existing event linking approaches usually adopt a retrieve-and-rank framework, which suffers from high computational costs and relies on hand-crafted rules, thereby limiting generalization. Additionally, it is found that some entity linking methods can be used to solve this task directly. However, they also perform not well. In this paper, we propose SEFEL, an end-to-end, argument-aware event representation-based event linking framework to unify the modeling of both in-KB and out-of-KB scenarios. To further enhance the linking performance, we propose a contrastive learning module to refine the learned embeddings of events and event mentions. Experimental results demonstrate that SEFEL improves accuracy by at least 3.59 (in-KB) and 21.5 (out-of-KB) compared with baselines, while its inference speed is more than 38 times faster than baselines, showcasing its accuracy and efficiency.

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AAAI Conference 2026 Conference Paper

Self-Improving Sparse Retrieval Through Heuristic Representation Refinement and Representation-Focused Learning

Xiaojing Li
Bin Wang
Xiaochun Yang
Meng Luo

Learnable sparse retrieval (LSR) models encode texts into high-dimensional sparse representations, supporting token-level expansion beyond the original text and addressing the vocabulary mismatch problem in traditional bag-of-words retrieval. However, in the absence of representation-level supervision, these representations usually overemphasize irrelevant tokens while neglecting truly relevant ones. We term this phenomenon the Representation Hallucination problem in LSR models, a critical bottleneck impeding accurate retrieval. To address this challenge, we introduce SiRe, a self-improving training framework for sparse retrieval that integrates two core strategies: Heuristic Representation Refinement and Representation-Focused Learning. Specifically, SiRe first identifies and corrects representation hallucinations in the outputs of the current LSR model using heuristic methods. The resulting representations serve as the primary supervision signals, guiding a pretrained language model (e.g., BERT) to mitigate the problem directly at the representation level. This process can be iterated, enabling progressive model improvement. Extensive experiments on both in-domain and out-domain benchmarks show that SiRe produces higher-quality sparse representations, significantly enhancing retrieval performance over strong baselines.

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TIST Journal 2025 Journal Article

Adaptive Intention Learning for Session-Based Recommendation

Qingbo Zhang
Xiaochun Yang
Hao Chen
Bin Wang
Zhu Sun
Xiangmin Zhou

In recent years, session-based recommender systems (SRSs) have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of SRSs. However, existing techniques assume that all sessions have the same number of intentions or that the items in one category belonging to the same session reflect the same intention. In real applications, such as e-commerce, sessions may have different numbers of intentions, and the same type of items in a session may correspond to different intentions. As a result, existing techniques cannot guarantee high-quality user interest prediction. In this article, we propose a novel Adaptive Intention Learning Network (AILN) to capture an adaptive number of intentions for each session, thereby enhancing the accuracy of user interest inference. Specifically, we design an intention evaluation network (IEN) to evaluate whether a subsequence of a session corresponds to a valid intention, and an intention generation network (IGN) to learn the representation of a valid intention. By checking each subsequence of a session, IEN and IGN enable the incremental learning of a session-specific intention hierarchy (IH) to store valid intentions of the session. To reduce the cost of building the IH, we propose a pruning strategy that exploits the intention validity to avoid unnecessary evaluation. The representative intentions are selected from IH and input into a designed interest predictor to infer the user interest. Experimental results on two real-world datasets demonstrate the superiority of our proposed AILN.

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AAAI Conference 2025 Conference Paper

LLM4RSR: Large Language Models as Data Correctors for Robust Sequential Recommendation

Yatong Sun
Xiaochun Yang
Zhu Sun
Yan Wang
Bin Wang
Xinghua Qu

Sequential Recommenders (SRs) are trained to predict the next item as the target given its preceding items as the input, assuming every input-target pair is matched and is reliable for training. However, users can be induced by external distractions to click on items inconsistent with their true preferences, resulting in unreliable training instances with mismatched input-target pairs. To resist unreliable data, researchers attempt to develop Robust SRs (RSRs). However, our data analysis unveils that existing RSRs are data-driven. That is, for most instances formed by infrequently co-occurred items, existing RSRs are uncertain about their reliability. To fill this gap, we propose a generic framework -- LLM4RSR (Large Language Models for Robust Sequential Recommendation) to semantically complement data-driven RSRs by correcting uncertain instances into reliable ones based on LLMs' semantic comprehension of items beyond co-occurrence. In this way, RSRs can be re-trained with the corrected data for better accuracy. This is a selective knowledge distillation procedure, where the LLM acts as a teacher guiding student RSRs via uncertain instances. To align LLMs with the data correction task and mitigate inherent hallucinations, we equip the LLM with profile, plan, and memory modules, which are automatically optimized via textual gradient descent, eliminating the need for human effort and expertise. Experiments on four real-world datasets spanning eight backbones verify the generality, effectiveness, and efficiency of LLM4RSR.

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AAAI Conference 2025 Conference Paper

Reverse Distribution Based Video Moment Retrieval for Effective Bias Elimination

Lingdu Kong
Xiaochun Yang
Tieying Li
Bin Wang
Xiangmin Zhou

Video Moment Retrieval (VMR) aims to identify a temporal segment in an untrimmed video that best matches a given textual query. Bias in VMR is a critical issue, where the model achieves favorable results even if disregarding the video input. Existing evaluation methods, such as Resplitting, have attempted to address bias by creating out-of-distribution (OOD) datasets. However, these methods provide an incomplete definition of bias and do not quantify bias. To this end, we provide a comprehensive definition of bias in VMR, encompassing both data bias and model bias. Besides, our evaluation metrics can analyze the magnitude of these biases better. To address both data and model biases comprehensively, we introduce Reverse Distribution based VMR (ReDis-VMR). This novel approach dynamically generates datasets with inverse distributions tailored to different models based on Gaussian kernel estimation. As a result, it enables a more accurate evaluation of model performance. Building on ReDis-VMR, we further propose the Dynamic Expandable Adjustment (DEA) pipeline. DEA incrementally expands the model structure to enhance its focus on video and text features, and it incorporates a fair loss to minimize the influence of concentrated data distributions. The experimental results on bias ratio demonstrate that our ReDis method achieves state-of-the-art performance in bias elimination, while the results on moment retrieval confirm the effectiveness of our DEA framework across three evaluation methods, two datasets, and three baselines.

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EAAI Journal 2024 Journal Article

Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems

Ziyan Zhao
Qi Jiang
Shixin Liu
MengChu Zhou
Xiaochun Yang
Xiwang Guo

Energy consumption, production cost, and efficiency are highly concerned by decision makers of energy-intensive and high-cost industrial production systems. Intelligent production scheduling is a necessary means to achieve their optimization. This work delves into a novel multi-objective production scheduling problem arising from a steel hot-rolling process, which is a representative energy-intensive and high-cost industrial process. The challenge of the problem involves scheduling customized production jobs subject to intricate process constraints with the goal to minimize three objective functions, i. e. , energy consumption, setup cost, and the number of tardy jobs. A mixed integer linear programming model is formulated for the problem. In order to solve it, an improved multi-objective evolutionary algorithm based on decomposition is presented. The algorithm incorporates problem-specific encoding and model-based decoding mechanisms, rendering it well-suited for addressing the concerned multi-constrained multi-objective optimization problem. The introduced modified Tchebycheff approach mitigates the impact of objective functions with varying value ranges on the algorithm’s convergence. Additionally, a Metropolis acceptance criterion is integrated to facilitate the escape from local optimal solutions, enhancing the algorithm’s global optimization capability. Numerous experiments are conducted to verify the effectiveness of the improvements and to compare the performance of the presented algorithm against its competitive peers. The results demonstrate its high performance, suggesting its significant potential for its application to steel hot-rolling systems.

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NeurIPS Conference 2023 Conference Paper

Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation

Yatong Sun
Bin Wang
Zhu Sun
Xiaochun Yang
Yan Wang

Sequential recommender systems (SRSs) are typically trained to predict the next item as the target given its preceding (and succeeding) items as the input. Such a paradigm assumes that every input-target pair is reliable for training. However, users can be induced to click on items that are inconsistent with their true preferences, resulting in unreliable instances, i. e. , mismatched input-target pairs. Current studies on mitigating this issue suffer from two limitations: (i) they discriminate instance reliability according to models trained with unreliable data, yet without theoretical guarantees that such a seemingly contradictory solution can be effective; and (ii) most methods can only tackle either unreliable input or targets but fail to handle both simultaneously. To fill the gap, we theoretically unveil the relationship between SRS predictions and instance reliability, whereby two error-bounded strategies are proposed to rectify unreliable targets and input, respectively. On this basis, we devise a model-agnostic Bidirectional Data Rectification (BirDRec) framework, which can be flexibly implemented with most existing SRSs for robust training against unreliable data. Additionally, a rectification sampling strategy is devised and a self-ensemble mechanism is adopted to reduce the (time and space) complexity of BirDRec. Extensive experiments on four real-world datasets verify the generality, effectiveness, and efficiency of our proposed BirDRec.

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AAAI Conference 2022 Conference Paper

Bi-CMR: Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval

Tieying Li
Xiaochun Yang
Bin Wang
Chong Xi
Hanzhong Zheng
Xiangmin Zhou

Cross-modal hashing has attracted considerable attention for large-scale multimodal data. Recent supervised cross-modal hashing methods using multi-label networks utilize the semantics of multi-labels to enhance retrieval accuracy, where label hash codes are learned independently. However, all these methods assume that label annotations reliably reflect the relevance between their corresponding instances, which is not true in real applications. In this paper, we propose a novel framework called Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval (Bi-CMR), which exploits a bidirectional learning to relieve the negative impact of this assumption. Specifically, in the forward learning procedure, we highlight the representative labels and learn the reinforced multi-label hash codes by intra-modal semantic information, and further adjust similarity matrix. In the backward learning procedure, the reinforced multi-label hash codes and adjusted similarity matrix are used to guide the matching of instances. We construct two datasets with explicit relevance labels that reflect the semantic relevance of instance pairs based on two benchmark datasets. The Bi-CMR is evaluated by conducting extensive experiments over these two datasets. Experimental results prove the superiority of Bi-CMR over four state-of-the-art methods in terms of effectiveness.

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IS Journal 2021 Journal Article

Adversarial Path Sampling for Recommender Systems

Rui Ding
Bowei Chen
Guibing Guo
Xiaochun Yang

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose path2rec a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e. g. , social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed path2rec by applying it into top- n item recommendation, which reaches better performance than other counterparts.

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IJCAI Conference 2021 Conference Paper

Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

Yatong Sun
Bin Wang
Zhu Sun
Xiaochun Yang

Most sequential recommender systems (SRSs) predict next-item as target for each user given its preceding items as input, assuming that each input is related to its target. However, users may unintentionally click on items that are inconsistent with their preference. We empirically verify that SRSs can be misguided with such unreliable instances (i. e. targets mismatch inputs). This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. Accordingly, BERD models both loss and uncertainty of each instance via a Gaussian distribution to better distinguish unreliable instances; meanwhile an uncertainty-aware graph convolution network is exploited to assist in mining unreliable instances by lowering uncertainty. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed BERD.

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IJCAI Conference 2019 Conference Paper

Discrete Trust-aware Matrix Factorization for Fast Recommendation

Guibing Guo
Enneng Yang
Li Shen
Xiaochun Yang
Xiaodong He

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

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IJCAI Conference 2019 Conference Paper

Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust

Qitian Wu
Lei Jiang
Xiaofeng Gao
Xiaochun Yang
Guihai Chen

Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.

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IJCAI Conference 2018 Conference Paper

An Adaptive Hierarchical Compositional Model for Phrase Embedding

Bing Li
Xiaochun Yang
Bin Wang
Wei Wang
Wei Cui
Xianchao Zhang

Phrase embedding aims at representing phrases in a vector space and it is important for the performance of many NLP tasks. Existing models only regard a phrase as either full-compositional or non-compositional, while ignoring the hybrid-compositionality that widely exists, especially in long phrases. This drawback prevents them from having a deeper insight into the semantic structure for long phrases and as a consequence, weakens the accuracy of the embeddings. In this paper, we present a novel method for jointly learning compositionality and phrase embedding by adaptively weighting different compositions using an implicit hierarchical structure. Our model has the ability of adaptively adjusting among different compositions without entailing too much model complexity and time cost. To the best of our knowledge, our work is the first effort that considers hybrid-compositionality in phrase embedding. The experimental evaluation demonstrates that our model outperforms state-of-the-art methods in both similarity tasks and analogy tasks.

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AAAI Conference 2017 Conference Paper

Efficiently Mining High Quality Phrases from Texts

Bing Li
Xiaochun Yang
Bin Wang
Wei Cui

Phrase mining is a key research problem for semantic analysis and text-based information retrieval. The existing approaches based on NLP, frequency, and statistics cannot extract high quality phrases and the processing is also time consuming, which are not suitable for dynamic on-line applications. In this paper, we propose an efﬁcient high-quality phrase mining approach (EQPM). To the best of our knowledge, our work is the ﬁrst effort that considers both intra-cohesion and inter-isolation in mining phrases, which is able to guarantee appropriateness. We also propose a strategy to eliminate order sensitiveness, and ensure the completeness of phrases. We further design efﬁcient algorithms to make the proposed model and strategy feasible. The empirical evaluations on four real data sets demonstrate that our approach achieved a considerable quality improvement and the processing time was 2. 3× ∼ 29× faster than the state-of-the-art works.

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