Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in Interactive Recommender Systems (IRSs). However, most of the existing RL-based IRSs usually face large discrete action space problem, which severely limits their efficiency. Moreover, data sparsity is another problem that most IRSs are confronted with. The utilization of recommendation-related textual knowledge can tackle this problem to some extent, but existing RL-based recommendation methods either neglect to combine textual information or are not suitable for incorporating it. To address these two problems, in this article, we propose a T ext-based deep R einforcement learning framework using self-supervised G raph representation for I nteractive R ecommendation (TRGIR). Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data sparsity problem. Moreover, we design an effective method to construct an action candidate set, which reduces the scale of the action space directly. Two types of representative reinforcement learning algorithms have been applied to implement TRGIR. Since the action space of IRS is discrete, it is natural to implement TRGIR with Deep Q-learning Network (DQN). In the TRGIR implementation with Deep Deterministic Policy Gradient (DDPG), denoted as TRGIR-DDPG, we design a policy vector, which can represent user’s preferences, to generate discrete actions from the candidate set. Through extensive experiments on three public datasets, we demonstrate that TRGIR-DDPG achieves state-of-the-art performance over several baselines in a time-efficient manner.

Chinese Entity Recognition (CER) aims to extract key information entities from Chinese text data, supporting subsequent natural language processing tasks such as relation extraction, knowledge graph construction, and intelligent question answering. However, CER faces several challenges, including limited training corpora, unclear entity boundaries, and complex entity structures, resulting in low accuracy and a call for further improvements. To address issues such as high annotation costs and ambiguous entity boundaries, this paper proposes the SEMFF-CER model, a CER model based on semantic enhancement and multi-feature fusion. The model employs character feature extraction algorithms, SofeLexicon semantic enhancement for vocabulary feature extraction, and deep semantic feature extraction from pre-trained models. These features are integrated into the entity recognition process via gating mechanisms, effectively leveraging diverse features to enhance contextual semantics and improve recognition accuracy. Additionally, the model incorporates several optimization strategies: an adaptive loss function to balance negative samples and improve the F1 score, data augmentation to enhance model robustness, and dropout and Adamax optimization algorithms to refine training. The SEMFF-CER model is characterized by a low dependence on training corpora, fast computation speed, and strong scalability. Experiments conducted on four Chinese benchmark entity recognition datasets validate the proposed model, demonstrating superior performance over existing models with the highest F1 score.

With the expansion of the scale and complexity of multimedia software, the detection of software defects has become a research hotspot. Because of the large scale of the existing software code, the efficiency and accuracy of the existing software defect detection algorithms are relatively low. We propose an intelligent memory leak detection scheme MLD based on defect modes in software. Based on the analysis of existing memory leak defect modes, we summarize memory operation behaviors (allocation, release and transfer) and present a state machine model. We employ a fuzzy matching algorithm based on regular expression to determine the memory operation behaviors and then analyze the change in the state machine to assess the vulnerability in the source code. To improve the efficiency of detection and solve the problem of repeated detection at the function call point, we propose a function summary method for memory operation behaviors. The experimental results demonstrate that the method we proposed has high detection speed and accuracy. The algorithm we proposed can identify the defects of the software, reduce the risk of being attacked to ensure safe operation.

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs.