Improved collaborative filtering with intensity-based contraction

Cui, Baojiang; Jiang, Haifeng; Liu, Zheli; Deng, Jiangdong

doi:10.1007/s12652-015-0284-9

Cited by 8 publications

(2 citation statements)

References 24 publications

(12 reference statements)

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“…These features can capture the semantic and contextual information of the text and provide input for subsequent recommendation algorithms.As shown in Figure 1, the execution flow of the proposed algorithm for the calculation area is as follows: Recommendation model construction:The intellectual property retrieval model based on recommendation algorithm can adopt a variety of algorithms, such as collaborative filtering, content recommendation, deep learning, etc. These algorithms can make use of existing intellectual property data to build recommendation models according to users' query intention and text features [7]. The goal of the model is to recommend relevant IP documents or information to users according to their query requirements.As shown in Formula 2, is the gradient calculation process in the directional propagation algorithm:…”

Section: Jewelry Design Methods Based On Genetic Algorithmmentioning

confidence: 99%

Research on Intellectual Property Retrieval based on intelligent recommendation algorithm

Yu,

Sun,

Samaryn

2023

Atlantis Highlights in Computer Sciences

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Effective retrieval of intellectual property is of great significance for promoting innovation and protecting intellectual property rights and interests.Aiming at the problems of information overload and low accuracy existing in traditional retrieval methods of intellectual property, this paper proposes an innovative intellectual property model based on recommendation algorithm, aiming at exploring intellectual property retrieval methods based on intelligent recommendation algorithm. To improve the retrieval efficiency and accuracy. The process mainly includes: data preprocessing, feature extraction and recommendation model construction. Firstly, the intelligent recommendation algorithm is summarized and the application potential of intelligent recommendation algorithm in intellectual property retrieval is analyzed. Then it introduces the key problems in intellectual property retrieval, including keyword extraction, document representation and similarity calculation. Then, the intellectual property retrieval model based on intelligent recommendation algorithm is elaborated. Finally, the performance of intellectual property retrieval method based on intelligent recommendation algorithm is verified by experiments, and compared with the traditional method. The experimental results show that the intellectual property retrieval method based on intelligent recommendation algorithm has high retrieval efficiency and accuracy, can provide personalized intellectual property information recommendation service, and provide strong support for the protection and application of intellectual property.

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Section: Jewelry Design Methods Based On Genetic Algorithmmentioning

confidence: 99%

Research on Intellectual Property Retrieval based on intelligent recommendation algorithm

Yu,

Sun,

Samaryn

2023

Atlantis Highlights in Computer Sciences

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“…After forwarding by the server, the remote monitoring device can call flexibly. Different terminal videos can be used to view the current surveillance video in real time, meeting the requirements of common video surveillance [7] .…”

Section: Face Recognition Algorithm Based On Deep Learningmentioning

confidence: 99%

Research on face recognition based on deep learning

Kang

2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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With the need of application upgrading in the field of computer vision and pattern recognition, dynamic face recognition technology has also been rapidly developed. At present, there are many face recognition processing methods in the field of video surveillance Only in the front end of the device to do simple recognition and tracking, a large number of complex operations, including face eigenvalue comparison and big data processing part are completed in the server side. For users of video surveillance, the method can not display the face recognition and comparison results in the monitoring terminal in real time. Aiming at this demand, this paper proposes a face recognition video transmission scheme based on edge computing to realize video transmission. The synchronization of face recognition effect between the acquisition and the player in the video. In this scheme, the video processing module of the terminal device simultaneously carries out video coding and face recognition to the video data, according to the terminal face. Database, extract and analyze the face recognition results and record the coordinate information in the video, and the synchronous packet mechanism is used to fuse the two, so as to obtain the video data package with both encoded data and face information. Through the verification system test, it is proved that the scheme can solve the synchronization problem of face frame display and tracking in live video, and has high accuracy and real-time performance.

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Autonomous Learning Recommendation Algorithm Based on K-Means and K-Nearest Neighbor

Wang

2019

Advances in Intelligent Systems and Computing

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Improved collaborative filtering with intensity-based contraction

Cited by 8 publications

References 24 publications

Research on Intellectual Property Retrieval based on intelligent recommendation algorithm

Research on Intellectual Property Retrieval based on intelligent recommendation algorithm

Research on face recognition based on deep learning

Autonomous Learning Recommendation Algorithm Based on K-Means and K-Nearest Neighbor

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