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Zhong Ming

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AAAI Conference 2026 Conference Paper

M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction

  • Yuze Zhang
  • Lingjie Li
  • Qiuzhen Lin
  • Zhong Ming
  • Fei Yu
  • Victor C. M. Leung

The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits the ability to fully understand and analyze hyperspectral images; (2) Single-scale feature extraction struggles to capture the complex structures and fine details present in hyperspectral images. To address these issues, we propose a multi-scale, multi-perceptual Mamba architecture for the spectral reconstruction task, called M3SR. Specifically, we design a multi-perceptual fusion block to enhance the ability of the model to comprehensively understand and analyze the input features. By integrating the multi-perceptual fusion block into a U-Net structure, M3SR can effectively extract and fuse global, intermediate, and local features, thereby enabling accurate reconstruction of hyperspectral images at multiple scales. Extensive quantitative and qualitative experiments demonstrate that the proposed M3SR outperforms existing state-of-the-art methods while incurring a lower computational cost.

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TIST Journal 2025 Journal Article

Cross-User Federated Recommendation Unlearning

  • Yang Li
  • Enyue Yang
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Cross-user federated recommendation (CUFR) is a promising solution for providing personalized services without collecting users’ raw data. However, most previous CUFR works mainly focus on providing accurate and privacy-preserving personalized recommendations, but overlook the fact that users can opt out at any time during the training process. In response, we study an emerging and new problem of efficiently training an unlearned model to forget the data of the clients who leave a federated system. It is challenging to simply apply or slightly modify existing machine unlearning or federated unlearning methods to CUFR because of the unique collaboration effect in recommender systems. Although a recent gradient calibration-based method (i.e., FRU) shows promising in training an unlearned model, there are still some limitations: (i) there is a potential possibility that some clients run out of the storage space, (ii) all the remaining clients need to participate in computing the new gradients, (iii) it masks the uniqueness of the local gradients, and (iv) the errors of the calibrated gradients will increase gradually with more iterations. In this article, we propose a novel CUFR unlearning (CUFRU) method. Specifically, we design a gradient transfer station (GTS) module for storing the historical gradients while enabling clients to dynamically participate in the computation of the calibrated gradients with the new gradients based on their online status. Moreover, we design a novel iteration-aware gradient calibration mechanism to strike a balance between the weights of the historical and new gradients at the different stages of the unlearning process, alleviating the calibration errors. Finally, we conduct extensive experiments on three real-world datasets to show that our CUFRU can more efficiently train an unlearned model with the competitive recommendation performance.

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EAAI Journal 2024 Journal Article

A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization

  • Yulong Ye
  • Qiuzhen Lin
  • Ka-Chun Wong
  • Jianqiang Li
  • Zhong Ming
  • Carlos A. Coello Coello

Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar subproblems, which are then optimized in a collaborative manner. However, when tackling imbalanced MOPs, the performance of most MOEA/Ds will evidently deteriorate, as a few solutions will replace most of the others in the evolutionary process, resulting in a significant loss of diversity. To address this issue, this paper suggests a localized decomposition evolutionary algorithm (LDEA) for imbalanced MOPs. A localized decomposition method is proposed to assign a local region for each subproblem, where the inside solutions are associated and the solution update is restricted inside (i. e. , solutions are only replaced by offspring within the same local region). Once off-spring are generated within an originally empty region, the best one is reserved for this subproblem to extend diversity. Meanwhile, the subproblem with the largest number of associated solutions will be found and one of its associated solutions with the worst aggregated value will be removed. Moreover, to speed up convergence for each subproblem while balancing the population's diversity, LDEA only evolves the best-associated solution in each subproblem and correspondingly tailors two decomposition methods in the environmental selection. When compared to nine competitive MOEAs, LDEA has shown the advantages in tackling two benchmark sets of imbalanced MOPs, one benchmark set of balanced yet complicated MOPs, and one real-world MOP.

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IJCAI Conference 2024 Conference Paper

A Survey on Cross-Domain Sequential Recommendation

  • shu chen
  • Zitao Xu
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we initially define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we specifically discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.

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TIST Journal 2024 Journal Article

Decentralized Federated Recommendation with Privacy-aware Structured Client-level Graph

  • Zhitao Li
  • Zhaohao Lin
  • Feng Liang
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, which may cause privacy issues. Recently, some centralized federated recommendation models are proposed for the protection of users’ privacy, which however requires a server for coordination in the whole process of model training. As a response, we propose a novel privacy-aware decentralized federated recommendation (DFedRec) model, which is lossless compared with the traditional model in recommendation performance and is thus more accurate than other models in this line. Specifically, we design a privacy-aware structured client-level graph for the sharing of the model parameters in the process of model training, which is a one-stone-two-bird strategy, i.e., it protects users’ privacy via some randomly sampled fake entries and reduces the communication cost by sharing the model parameters only with the related neighboring users. With the help of the privacy-aware structured client-level graph, we propose two novel collaborative training mechanisms in the setting without a server, including a batch algorithm DFedRec(b) and a stochastic one DFedRec(s), where the former requires the anonymity mechanism while the latter does not. They are both equivalent to probabilistic matrix factorization trained in a centralized server and are thus lossless. We then provide formal analysis of privacy guarantee of our methods and conduct extensive empirical studies on three public datasets with explicit feedback, which show the effectiveness of our DFedRec, i.e., it is privacy aware, communication efficient, and lossless.

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AIJ Journal 2023 Journal Article

Privacy-preserving graph convolution network for federated item recommendation

  • Pengqing Hu
  • Zhaohao Lin
  • Weike Pan
  • Qiang Yang
  • Xiaogang Peng
  • Zhong Ming

In traditional recommender systems, we often build models based on a centralized storage of user data, which however will lead to user privacy concerns and risks. In this paper, we study an emerging and important recommendation problem called federated item recommendation (FIR), in which a recommendation model is built with decentralized data of user-item interactions in a privacy-aware manner, i. e. , the personal behavior data of each user does not leave the owner. Recently, graph neural network (GNN) has been widely recognized as a state-of-the-art solution for item recommendation with implicit feedback since it is able to model the high-order connectivity between users and items. However, it is very challenging to exploit the high-order connectivity information in a decentralized user-item interaction graph without compromising user privacy. To address that, we propose a GNN-based federated recommendation framework, i. e. , privacy-preserving graph convolution network (P-GCN), for the studied problem of FIR. Our P-GCN can leverage the high-order connectivity information like a centralized GCN model such as LightGCN, though it is built using a decentralized user-item graph. To achieve that, we design a novel privacy-preserving graph convolution approach based on secure aggregation and employ item-based user representation to compensate for the performance loss it causes due to the protection of user privacy. Moreover, we improve a group-wise concealing strategy for protecting user privacy. Empirical studies on three datasets show that our P-GCN can achieve similar or even better performance comparing with the non-federated (i. e. , centralized) counterpart, and outperforms all the existing federated methods for the studied problem.

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AIJ Journal 2023 Journal Article

Transfer learning for collaborative recommendation with biased and unbiased data

  • Zinan Lin
  • Dugang Liu
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

In a recommender system, a user's interaction is often biased by the items' displaying positions and popularity, as well as the user's self-selection. Most existing recommendation models are built using such a biased user-system interaction data alone. In this paper, we introduce an additional specially collected unbiased data, and then have a new problem called collaborative recommendation with biased and unbiased data. We first formalize the studied problem and list three challenges, including the bias challenge, the heterogeneity challenge and the unbalance challenge. Then we propose a novel transfer learning-based AI solution, i. e. , transfer via joint reconstruction (TJR), to achieve knowledge transfer and sharing between the biased data and unbiased data. Specifically, in our TJR, we use two different models to extract the users' preferences and bias information, and then refine the prediction via the latent features containing the bias information in order to obtain a more accurate and unbiased recommendation. We further integrate the two data by reconstructing their interaction in a joint learning manner. Moreover, in order to better address the unbalance challenge, we introduce a bias regularization term and integrate bidirectional knowledge distillation. Finally, we adopt four representative methods, i. e. , variational autoencoders, matrix factorization, neural collaborative filtering and graph convolution network, as the backbone models of our TJR and conduct extensive empirical studies on three public datasets, showcasing the effectiveness of our transfer learning solution over some very competitive baselines.

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EAAI Journal 2022 Journal Article

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

  • Lingjie Li
  • Yongfeng Li
  • Qiuzhen Lin
  • Zhong Ming
  • Carlos A. Coello Coello

Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategies for most existing PSOs are not so efficient in high-dimensional objective space, mainly due to the lack of convergence pressure or loss of diversity. In order to address these two issues and improve the performance of PSO in high-dimensional objective space, this paper proposes a convergence and diversity guided leader selection strategy for PSO, denoted as CDLS, in which different leader particles are adaptively selected for each particle based on its corresponding situation of convergence and diversity. In this way, a good tradeoff between the convergence and diversity can be achieved by CDLS. To verify the effectiveness of CDLS, it is embedded into the PSO search process of three well-known PSOs. Furthermore, a new variant of PSO combining with the CDLS strategy, namely PSO/CDLS, is also presented. The experimental results validate the superiority of our proposed CDLS strategy and the effectiveness of PSO/CDLS, when solving numerous MaOPs with regular and irregular Pareto fronts ( PF s).

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AAMAS Conference 2022 Conference Paper

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms

  • Biyang Ma
  • Yinghui Pan
  • Yifeng Zeng
  • Zhong Ming

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms.

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TIST Journal 2022 Journal Article

FLAG: A Feedback-aware Local and Global Model for Heterogeneous Sequential Recommendation

  • Mingkai He
  • Jing Lin
  • Jinwei Luo
  • Weike Pan
  • Zhong Ming

Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users’ complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user’s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user’s global preference; (iii) a local intention learning module, which takes a user’s real feedback in the next step, i.e., the user’s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user’s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.

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AAMAS Conference 2022 Conference Paper

LB f T: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems

  • Yinghui Pan
  • Junhan Chen
  • Yifeng Zeng
  • Zhangrui Yao
  • Qianwen Li
  • Biyang Ma
  • Yi Ji
  • Zhong Ming

Discovering variables and understanding their relations, which impacts emergency response, provide important knowledge to the development of decision models, e. g. Bayesian networks, in autonomous typhoon response systems (ATRS). Given the text inputs (containing natural language), learning the network structures still remains a challenge although learning Bayesian networks from data has been extensively investigated in various fields. In this demo, we develop a deep learning based framework for identifying typhoon relevant variables and build their causal relations from text. We use the CausalBank dataset and typhoon specific relation rules to refine the learned relations and allow users to further improve the models through their domain knowledge. We integrate the new learning tool into the existing ATRS and demonstrate the empirical results through real-world typhoon reports.

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IJCAI Conference 2022 Conference Paper

Multi-Constraint Deep Reinforcement Learning for Smooth Action Control

  • Guangyuan Zou
  • Ying He
  • F. Richard Yu
  • Longquan Chen
  • Weike Pan
  • Zhong Ming

Deep reinforcement learning (DRL) has been studied in a variety of challenging decision-making tasks, e. g. , autonomous driving. \textcolor{black}{However, DRL typically suffers from the action shaking problem, which means that agents can select actions with big difference even though states only slightly differ. } One of the crucial reasons for this issue is the inappropriate design of the reward in DRL. In this paper, to address this issue, we propose a novel way to incorporate the smoothness of actions in the reward. Specifically, we introduce sub-rewards and add multiple constraints related to these sub-rewards. In addition, we propose a multi-constraint proximal policy optimization (MCPPO) method to solve the multi-constraint DRL problem. Extensive simulation results show that the proposed MCPPO method has better action smoothness compared with the traditional proportional-integral-differential (PID) and mainstream DRL algorithms. The video is available at https: //youtu. be/F2jpaSm7YOg.

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AAMAS Conference 2021 Conference Paper

ATPT: Automate Typhoon Contingency Plan Generation from Text

  • Yifeng Zeng
  • Zhangrui Yao
  • Yinghui Pan
  • Wanqing Chen
  • Junxin Zhou
  • Junhan Chen
  • Biyang Ma
  • Zhong Ming

Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e. g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents.

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AAAI Conference 2021 Conference Paper

FedRec++: Lossless Federated Recommendation with Explicit Feedback

  • Feng Liang
  • Weike Pan
  • Zhong Ming

With the marriage of federated machine learning and recommender systems for privacy-aware preference modeling and personalization, there comes a new research branch called federated recommender systems aiming to build a recommendation model in a distributed way, i. e. , each user is represented as a distributed client where his/her original rating data are not shared with the server or the other clients. Notice that, besides the sensitive information of a specific rating score assigned to a certain item by a user, the information of a user’s rated set of items shall also be well protected. Some very recent works propose to randomly sample some unrated items for each user and then assign some virtual ratings, so that the server can not identify the scores and the set of rated items easily during the server-client interactions. However, the virtual ratings assigned to the randomly sampled items will inevitably introduce some noise to the model training process, which will then cause loss in recommendation performance. In this paper, we propose a novel lossless federated recommendation method (FedRec++) by allocating some denoising clients (i. e. , users) to eliminate the noise in a privacy-aware manner. We further analyse our FedRec++ in terms of security and losslessness, and discuss its generality in the context of existing works. Extensive empirical studies clearly show the effectiveness of our FedRec++ in providing accurate and privacy-aware recommendation without much additional communication cost.

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IS Journal 2021 Journal Article

FedRec: Federated Recommendation With Explicit Feedback

  • Guanyu Lin
  • Feng Liang
  • Weike Pan
  • Zhong Ming

Recommendation models have been widely embedded in various online services, while most of which are designed with the assumption that users’ original behaviors are available in a central server. This may cause the privacy issue. As a response, we follow a recent work called federated collaborative filtering (FCF) for item recommendation with implicit feedback and propose a novel and generic federated recommendation (FedRec) framework for rating prediction with explicit feedback. Specifically, we federate some basic and advanced factorization-based recommendation models both in batch style and in stochastic style. More importantly, in order to protect the private information of which items each user has rated, as well as not to significantly increase the computational and communication cost, we design two simple but effective strategies, i. e. , user averaging and hybrid filling, in which some (instead of all) unrated items are randomly sampled and assigned with some virtual ratings accordingly. Empirical studies on two public datasets show the effectiveness of our FedRec in terms of the closeness of a federated model and an unfederated one, and the usefulness of the two filling strategies.

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TIST Journal 2020 Journal Article

CoFi-points

  • Lin Li
  • Weike Pan
  • Zhong Ming

With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users’ preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative filtering (OCCF). For a long time, pairwise ranking-oriented learning scheme has been viewed as a superior solution than the pointwise scheme for OCCF due to its higher accuracy in most cases. However, we argue that with appropriate model design, pointwise preference learning can achieve comparable or even better performance than the counterpart, i.e., pairwise preference learning. In particular, we propose a new preference assumption, i.e., pointwise preference on user/item-set. Based on this new assumption, we develop a novel, simple, and flexible solution called collaborative filtering via pointwise preference learning on user/item-set (CoFi-points). Furthermore, we derive two specific algorithms of CoFi-points with respect to the involved user-set and item-set, i.e., CoFi-points(u) and CoFi-points(i), referring to preference assumptions defined on user-set and item-set, respectively. Finally, we conduct extensive empirical studies on four real-world datasets with the state-of-the-art methods, and find that our solution can achieve very promising performance with respect to several ranking-oriented evaluation metrics.

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IS Journal 2017 Journal Article

Collaborative Recommendation with Multiclass Preference Context

  • Weike Pan
  • Zhong Ming

Factorization- and neighborhood-based methods have been recognized as state-of-the-art approaches for collaborative recommendation tasks. In this article, the authors take user ratings as categorical multiclass preferences and propose a novel method called matrix factorization with multiclass preference context (MF-MPC), which integrates an enhanced neighborhood based on the assumption that users with similar past multiclass preferences (instead of one-class preferences in SVD++) will have similar tastes in the future. The main merit of MF-MPC is its ability to make use of the multiclass preference context in the factorization framework in a fine-grained manner and thus inherit the advantages of those two methods. Experimental results on three real-world datasets show that their solution can perform significantly better than factorization-based methods, neighborhood-based methods, and integrated methods with a one-class preference context.

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TAAS Journal 2017 Journal Article

Efficient and Robust Emergence of Norms through Heuristic Collective Learning

  • Jianye Hao
  • Jun Sun
  • Guangyong Chen
  • Zan Wang
  • Chao Yu
  • Zhong Ming

In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.

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TIST Journal 2017 Journal Article

Transfer Learning for Behavior Ranking

  • Weike Pan
  • Qiang Yang
  • Yuchao Duan
  • Ben Tan
  • Zhong Ming

Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users’ behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users’ true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking, which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.

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IS Journal 2016 Journal Article

Transfer Learning for Heterogeneous One-Class Collaborative Filtering

  • Weike Pan
  • Mengsi Liu
  • Zhong Ming

Various memory- and model-based collaborative filtering algorithms have been designed for multiclass feedback (such as grade scores) in the past two decades. Recently, one-class feedback (such as positive feedback and implicit examination) has been recognized as a more pervasive and important source of information in many real recommendation systems. Previous work along these lines mainly focus on homogenous one-class positive feedback, such as likes on Facebook or transactions on Amazon, which might not capture a user's true preferences due to the sparsity of such data. To alleviate this sparsity problem, the authors study positive feedback and implicit examinations simultaneously, coined as heterogeneous one-class collaborative filtering (HOCCF). Specifically, they designed a novel transfer learning algorithm for HOCCF, called transfer via joint similarity learning (TJSL), that jointly learns a similarity between a candidate item and a preferred item, and a similarity between a candidate item and an identified likely-to-prefer examined item. Joint similarity learning has the merit of being able to connect two seemingly unrelated items along sparse positive feedback only. Empirical studies on three real-world datasets show that TJSL can recommend items more accurately than other state-of-the-art methods.

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EAAI Journal 2014 Journal Article

An efficient and robust negotiating strategy in bilateral negotiations over multiple items

  • Jianye Hao
  • Songzheng Song
  • Ho-fung Leung
  • Zhong Ming

Multi-item negotiations surround our daily life and usually involve two parties that share common or conflicting interests. Effective automated negotiation techniques should enable the agents to adaptively adjust their behaviors depending on the characteristics of their negotiating partners and negotiation scenarios. This is complicated by the fact that the negotiation agents are usually unwilling to reveal their information (strategies and preferences) to avoid being exploited during negotiation. In this paper, we propose an adaptive negotiation strategy, called ABiNeS, which can make effective negotiations against different types of negotiating partners. The ABiNeS strategy employs the non-exploitation point to adaptively adjust the appropriate time to stop exploiting the negotiating partner and also predicts the optimal offer for the negotiating partner based on the reinforcement-learning based approach. Simulation results show that the ABiNeS strategy can perform more efficient exploitations against different types of negotiating partners, and thus achieve higher overall payoffs compared with the state-of-the-art strategies under negotiation tournaments. We also provide a detailed analysis of why the ABiNeS strategy can negotiate more efficiently compared with other existing state-of-the-art negotiation strategies focusing on two major components. Lastly, we propose adopting the single-agent best deviation principle to analyze the robustness of different negotiation strategies based on model checking techniques. Through our analysis, the ABiNeS strategy is shown to be very robust against other state-of-the-art strategies under different negotiation contexts.

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IS Journal 2014 Journal Article

Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedback

  • Weike Pan
  • Zhong Ming

A real recommender system can usually make use of more than one type of user feedback--for example, numerical ratings and binary ratings-to learn a user's true preferences. Recent work has proposed a transfer learning algorithm called transfer by collective factorization (TCF) to exploit such heterogeneous user feedback. TCF performs via sharing data-independent knowledge and modeling data-dependent effects simultaneously. However, TCF is a batch algorithm and updates the model parameters only once after scanning all data, which might not be efficient enough for real systems. This article proposes a novel and efficient transfer learning algorithm called interaction-rich transfer by collective factorization (iTCF), which extends the efficient collective matrix factorization (CMF) algorithm by providing more interactions between the user-specific latent features. The assumption under iTCF is that the predictability with regards to the same user's rating behaviors in two related data is likely to be similar. Considering the shared predictability, the authors derive novel update rules for iTCF in a stochastic algorithmic framework. The advantages of iTCF include its efficiency compared with TCF, and its higher prediction accuracy compared with CMF. Experimental results on three real-world datasets show the effectiveness of iTCF over the state-of-the-art methods.

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TAAS Journal 2014 Journal Article

Multiagent Reinforcement Social Learning toward Coordination in Cooperative Multiagent Systems

  • Jianye Hao
  • Ho-fung Leung
  • Zhong Ming

Most previous works on coordination in cooperative multiagent systems study the problem of how two (or more) players can coordinate on Pareto-optimal Nash equilibrium(s) through fixed and repeated interactions in the context of cooperative games. However, in practical complex environments, the interactions between agents can be sparse, and each agent's interacting partners may change frequently and randomly. To this end, we investigate the multiagent coordination problems in cooperative environments under a social learning framework. We consider a large population of agents where each agent interacts with another agent randomly chosen from the population in each round. Each agent learns its policy through repeated interactions with the rest of the agents via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy in such a situation. We distinguish two different types of learners depending on the amount of information each agent can perceive: individual action learner and joint action learner. The learning performance of both types of learners is evaluated under a number of challenging deterministic and stochastic cooperative games, and the influence of the information sharing degree on the learning performance also is investigated—a key difference from the learning framework involving repeated interactions among fixed agents.

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v2026.09.13