5 results about "Feature structure" patented technology

The invention discloses a recommendation system user behavior sequence feature enhancement method based on a big language model, which comprises the following steps: acquiring a recommendation data set, performing feature structure analysis according to the use of the recommendation data set in a recommendation system and a conventional field to construct a first text, and inputting the big language model to generate a data set description; constructing a second text according to the target task and the sequence feature type, and inputting the second text and the data set description into a large language model to generate feature semantics; constructing a third text based on the feature semantics, and inputting the large language model to generate feature codes; feature codes are embedded into a prediction model training environment, and automatic generation and loading of new features in training data are achieved; and carrying out a new feature experiment in the prediction model framework, and evaluating and generating a new round of sequence features. The method has higher expressive power, context perceptibility and task adaptability in a behavior data modeling-oriented recommendation system and a personalized system, and can effectively solve the technical bottlenecks of an existing method in the aspects of structure, expression, generalization and the like.

The application discloses a self-supervised heterogeneous knowledge graph learning method based on feature structure homogeneity and long-range heterogeneity. First, a self-expression solver is proposed, which can capture the complementary homogeneity between meta-path and node features, and then obtain homogeneity representation. At the same time, the application designs a path encoder to model various interaction relationships. Through adaptive fusion, the cross-type interaction relationship is explicitly included while reducing noise interference, and long-range heterogeneity is captured. Theoretical analysis verifies that the homogeneity representation has a high-order grouping effect and can effectively capture complementary homogeneity; the path encoder has adaptive smoothing capability to filter noise; and cross-type interaction modeling integrates homogeneity and heterogeneity to introduce more task-related information. Experiments on various data sets including large-scale data sets fully verify the superiority of the method.

The invention discloses a method for relieving catastrophic forgetting of incremental target detection based on incremental relationship consistency, and relates to the technical field of catastrophic forgetting. According to the method, disastrous forgetting is relieved, and under the condition that old task data is not repeatedly used, the old category detection performance is stably reserved when a new category is learned; knowledge offset is eliminated, dependency of the model on task specific semantics is reduced by learning cross-task relation consistency, and semantic differences of different tasks are adapted; the feature structure is stabilized, the category boundary is kept clear through feature distribution constraint, and new category features are prevented from damaging the old task feature space; the cross-task adaptability is improved, intra-class and inter-class relation learning is enhanced, and the generalization ability of the model in single-step and multi-step increment scenes is enhanced; moreover, the training process is simplified, a collaborative optimization framework is designed, the training efficiency and performance are considered, and the actual application of the incremental detection technology is promoted.

The invention relates to the technical field of security development, and provides a code defect analysis method, which is used for improving the code defect analysis efficiency and reducing the workload of manual review. The method comprises the following steps: acquiring a code defect report and appeal data; extracting a first feature structure from the code snippet, and generating a composite fingerprint based on the first feature structure; when it is determined that no fingerprint matched with the composite fingerprint exists in a pre-stored composite fingerprint library, in a pre-stored feature structure library, determining a similar feature structure of which the similarity with the first feature structure meets a preset condition, and determining a first false alarm probability of the code snippet; analyzing the code defect report and the appeal data by using a pre-trained large language model, and determining a second false alarm probability of the code snippets and a rationality score of the appeal data; and determining a defect misinformation evaluation value of the code snippet, and determining that the code snippet does not have defects under the condition that the defect misinformation evaluation value is greater than or equal to a first preset threshold value.

The application relates to the technical field of text processing, in particular to an advertisement copy automatic generation method based on artificial intelligence, which comprises the following steps: collecting advertisement copies in a traffic delivery platform, identifying high-quality copies in the platform, determining the copy feature structure of the high-quality copies, respectively analyzing user keywords of access users and transaction users, determining user demand labels in different platforms, extracting key information words from function information of a target product based on the key demand labels of the users, identifying corresponding matching information words in the key information words of the target product based on the text structure of the high-quality copies, combining the identified matching information words according to the text structure of the high-quality copies, and automatically generating advertisement copies. The application automatically collects and analyzes the feature structure of high-quality copies in the platform through artificial intelligence, improves the production efficiency of the copies, and further improves the adaptability to the reading habits of the users of the platform based on the copies generated according to the feature structure of the platform.