High-precision control of constrained robot manipulators under uncertainties remains a fundamental challenge. While conventional nonlinear model predictive control (NMPC) often struggles with real-time requirements due to its heavy computational burden, linear MPC (LMPC) typically relies on terminal invariant sets that are often computationally intractable for complex nonlinear systems. To address these limitations, this paper proposes a computationally efficient data-driven linear model predictive control (DLMPC) framework that achieves control accuracy comparable to NMPC while ensuring real-time performance. A variable-length sliding-window dynamic mode decomposition with control (DMDc) method is developed to identify a time-varying local affine model from recent input–output data, enabling accurate linearization under uncertainties. Based on this model, a novel time-varying terminal constraint is designed to substitute the conventional terminal set, thereby obviating the need for uncertainty upper bounds that are difficult to obtain in practice. The recursive feasibility and stability of the proposed framework are established using Lyapunov stability theory. Finally, experimental results on a Franka Emika Panda robot demonstrate the effectiveness and superior performance of the proposed method.

Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i.e., it considers popularity from a global perspective of all users and uses a single set of popular items, and thus cannot capture the interests of individual users. As such, we propose a user-aware version of item popularity named personal popularity (PP), which identifies different popular items for each user by considering the users that share similar interests. As PP models the preferences of individual users, it naturally helps to produce personalized recommendations and mitigate GP bias. To integrate PP into recommendation, we design a general personal popularity aware counterfactual (PPAC) framework, which adapts easily to existing recommendation models. In particular, PPAC recognizes that PP and GP have both direct and indirect effects on recommendations and controls direct effects with counterfactual inference techniques for unbiased recommendations. All codes and datasets are available at https://github.com/Stevenn9981/PPAC.

Entity alignment (EA), a crucial task in knowledge graph (KG) research, aims to identify equivalent entities across different KGs to support downstream tasks like KG integration, text-to-SQL, and question-answering systems. Given rich semantic information within KGs, pre-trained language models (PLMs) have shown promise in EA tasks due to their exceptional context-aware encoding capabilities. However, the current solutions based on PLMs encounter obstacles such as the need for extensive training, expensive data annotation, and inadequate incorporation of structural information. In this study, we introduce a novel zero-training EA framework, ZeroEA, which effectively captures both semantic and structural information for PLMs. To be specific, Graph2Prompt module serves as the bridge between graph structure and plain text by converting KG topology into textual context suitable for PLM input. Additionally, in order to provide PLMs with concise and clear input text of reasonable length, we design a motif-based neighborhood filter to eliminate noisy neighbors. The comprehensive experiments and analyses on 5 benchmark datasets demonstrate the effectiveness of ZeroEA, outperforming all leading competitors and achieving state-of-the-art performance in entity alignment. Notably, our study highlights the considerable potential of EA technique in improving the performance of downstream tasks, thereby benefitting the broader research field.

Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.

Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs,meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. (RMS) is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by (RMS) consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.

In this paper, we propose the streaming MaxBRNNquery, which finds the optimal region to deploy a new service point when both the service points and client points are under continuous updates. The streaming MaxBRNN query has many applications such as taxi scheduling, shared bike placements, etc. Existing MaxBRNN solutions are insufficient for streaming updates as they need to re-run from scratch even for a small amount of updates, resulting in long query processing time. To tackle this problem, we devise an efficient slot partitioning-based algorithm (SlotP), which divides the space into equal-sized slots and processes each slot independently. The superiorities of our proposal for streaming MaxBRNN query are: (i) an update affects only a smaller number of slots and works done on the unaffected slots can be reused directly; (ii) the influence value upper bound of each slot can be derived efficiently and accurately, which facilitate pruning many slots from expensive computation. We conducted extensive experiments to validate the performance of the SlotP algorithm. The results show that SlotP is 2-3 orders of magnitude faster than state-of-the-art baselines.

Recently, the spatial-temporal data of urban moving objects, e.g., cars and buses, are collected and widely used in urban trajectory exploratory analysis. Urban bus service is one of the most common public transportation services. Urban bus data analysis plays an important role in smart city applications. For example, data analysts in bus companies use the urban bus data to optimize their bus scheduling plan. Map services providers, e.g., Google map, Ten-cent map, take urban bus data into account to improve their service quality (e.g., broadcast road update instantly). Unlike urban moving cars or pedestrians, urban buses travel on known bus routes. The operating buses form the "bus flows" in a city. Efficient analyzing urban bus flows has many challenges, e.g., how to analyze the dynamics of given bus routes? How to help users to identify traffic flow of interests easily? In this work, we present CheetahVIS, a visual analytical system for efficient massive urban bus data analysis. CheetahVIS builds upon Spark and provides a visual analytical platform for the stakeholders (e.g., city planner, data analysts in bus company) to conduct effective and efficient analytical tasks. In the demonstration, demo visitors will be invited to experience our proposed CheetahVIS system with different urban bus data analytical functions, e.g., bus route analysis, public bus flow overview, multiple region analysis, in a real-world dataset. We also will present a case study, which compares different regions in a city, to demonstrate the effectiveness of CheetahVIS.