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Open Access Issue
Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation
Tsinghua Science and Technology 2025, 30(2): 585-599
Published: 09 December 2024
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Integrating Knowledge Graphs (KGs) into recommendation systems as supplementary information has become a prevalent strategy. By leveraging the semantic relationships between entities in KGs, recommendation systems can better comprehend user preferences. Due to the unique structure of KGs, methods based on Graph Neural Networks (GNNs) have emerged as the current technical trend. However, existing GNN-based methods struggle to (1) filter out noisy information in real-world KGs, and (2) differentiate the item representations obtained from the knowledge graph and bipartite graph. In this paper, we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation (namely AKDN) that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite graphs. In particular, we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs, which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the items. Furthermore, we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information, and leverage dual item representations to fuse them together for better capturing user behavior patterns. We conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods, like Knowledge Graph Attention Network (KGAT) and Knowledge Graph-based Intent Network (KGIN).

Open Access Article Issue
Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods
CAAI Artificial Intelligence Research 2024, 3: 9150033
Published: 08 May 2024
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Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

Open Access Issue
Approximation Algorithm for the Balanced 2-Correlation Clustering Problem
Tsinghua Science and Technology 2022, 27(5): 777-784
Published: 17 March 2022
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The Correlation Clustering Problem (CorCP) is a significant clustering problem based on the similarity of data. It has significant applications in different fields, such as machine learning, biology, and data mining, and many different problems in other areas. In this paper, the Balanced 2-CorCP (B 2-CorCP) is introduced and examined, and a new interesting variant of the CorCP is described. The goal of this clustering problem is to partition the vertex set into two clusters with equal size, such that the number of disagreements is minimized. We first present a polynomial time algorithm for the B 2-CorCP on M-positive edge dominant graphs (M3). Then, we provide a series of numerical experiments, and the results show the effectiveness of our algorithm.

Open Access Issue
Algorithms for the Prize-Collecting k-Steiner Tree Problem
Tsinghua Science and Technology 2022, 27(5): 785-792
Published: 17 March 2022
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In this paper, we study the prize-collecting k-Steiner tree (PC kST) problem. We are given a graph G=(V,E) and an integer k. The graph is connected and undirected. A vertex rV called root and a subset RV called terminals are also given. A feasible solution for the PC kST is a tree F rooted at r and connecting at least k vertices in R. Excluding a vertex from the tree incurs a penalty cost, and including an edge in the tree incurs an edge cost. We wish to find a feasible solution with minimum total cost. The total cost of a tree F is the sum of the edge costs of the edges in F and the penalty costs of the vertices not in F. We present a simple approximation algorithm with the ratio of 5.9672 for the PC kST. This algorithm uses the approximation algorithms for the prize-collecting Steiner tree (PCST) problem and the k-Steiner tree ( kST) problem as subroutines. Then we propose a primal-dual based approximation algorithm and improve the approximation ratio to 5.

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