Item life cycle based collaborative filtering

Liu, Yue; Cai, Fei; Ren, Pengfei; Gu, Zhizhou

doi:10.3233/jifs-18783

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…This paper designs a real-time face recognition video transmission scheme based on edge computing, terminal [3] . In real-time video transmission, the data carries the face frame information that displays the face detection result.…”

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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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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“…In the case of a user-based technique, this involves calculating the similarities of the user ratings on the same items forms the model. On the contrary, the item-based technique is constructed by calculating the similarities between the items [57,187,197].…”

Section: Memory-based Collaborative Filtering Techniquementioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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“…However, the traditional retrieval method of intellectual property has the problems of information overload and low accuracy, which is difficult to meet the actual demand. With the development and application of intelligent recommendation algorithm, the intellectual property retrieval method based on intelligent recommendation algorithm has gradually attracted attention, and shown a broad application prospect in practice [3].…”

Section: Introductionmentioning

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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Item life cycle based collaborative filtering

Cited by 5 publications

References 17 publications

Research on face recognition based on deep learning

Research on face recognition based on deep learning

Fashion Recommendation Systems, Models and Methods: A Review

Research on Intellectual Property Retrieval based on intelligent recommendation algorithm

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