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Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (IC...
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Citations
... Huang et al. [27] introduced an enhanced CF personalized recommendation "(ICF)" algorithm designed to effectively address the issue of "data sparsity" by minimizing the item space. By employing the "k-means clustering" method to extract similarity information secondarily, the "ICF" algorithm more accurately acquires user similarity information, thereby enhancing the precision of recommender systems. ...
In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.
... Change task -design team collaborative filtering recommendation system Design team recommendations for design tasks can be implemented through collaborative filtering algorithms. Collaborative filtering (CF) methods produce user-specific recommendations of items based on patterns of ratings or usage without the need for exogenous information about either items or users (Huang et al., 2023). Collaborative filtering is a commonly used technique in recommendation systems that provides personalized recommendations by analyzing user behavior and preferences (Liu et al., 2021). ...
Industrial design change task refers to the task resulting from industrial design schemes changing processes due to manufacturing needs in complex product development. In order to make full use of the experience generated in design design change, this paper proposes an industrial design change task allocation method based on implementation intention. Based on the action stage model, the connotation of the implementation intention of industrial design tasks is proposed. Based on the state of the design change, the design team and design task model are constructed through the Extenics theory. A quantitative model of implementation intention and an evaluation method for generalization are constructed. The similarity between the design tasks and the support of the design team for the design tasks are quantified and calculated by the complex network. For change design tasks other than design activities, the collaborative filtering algorithm is improved to achieve design team recommendations based on task similarity. In the process of collaborative filtering, the relationship between tasks is considered, and the time loss caused by the propagation of tasks among different teams is evaluated to improve the effectiveness of the task allocation scheme. Take the change in the internal facility manufacturing requirements of the intelligent cabin as an example. Compared to the design process before applying this method, the task load of design teams was reduced by 18.74 %, which proved this method can support industry design change problems.
... To solve this problem, the researchers proposed an improved collaborative filtering personalization algorithm. Experimental results showed that the algorithm had significant improvement in the accuracy and precision of recommendation [7]. The firefly algorithm, developed by Zhang F et al. and improved by KMA, can help maintain the power batteries in electric vehicles by addressing the issue of power battery voltage platform load overload. ...
Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e-commerce industry also experiences quick development. However, the recommendation technology of e-commerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e-commerce recommender systems, this research introduces an e-commerce recommender system that utilizes an enhanced K-means clustering algorithm to manage commodity information. This method combines the K-means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the K-mean clustering algorithm and fuzzy C-mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved K-mean clustering algorithm, which was 91.1 %. The convergence rate of the improved K-mean clustering algorithm was faster by 44 % compared to the traditional K-mean clustering algorithm and 73 % quicker than the fuzzy C-mean algorithm. The study's findings demonstrate that the refined K-means clustering algorithm greatly enhances the recommendation proficiency and precision of the e-commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e-commerce industry and stimulate its growth.
... Based on mass diffusion behavior, they proposed a new social recommendation model, which combined social network information with user-item information. These existing studies have improved the accuracy of the recommendation results, but there are still shortcomings in the accuracy and diversity of recommendation results [17][18][19]. ...
Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.
... Traditional saliency object detection algorithms mainly use image processing and computer vision technologies, such as edge detection, color space conversion, and feature extraction. In recent years, the development of deep learning and sensing technologies has brought significant improvements to saliency object detection, and using depth information for saliency detection has become more important [18]. The current RGB-D saliency detection algorithm exhibits low robustness in object target set image boundary recognition. ...
With the development of scientific information technology and the popularization of electronic devices, images and videos have become very important forms of information expression and carriers in our current lives. Accelerating the mining of valuable information content from massive data has become a very important aspect of current computer vision research. The saliency object detection method, which is related to human visual attention, is gradually being applied in computer processing. However, in current color depth models, the association mining of data depth clues is still far from sufficient, and there is still significant room for improvement in image quality. Based on this, an improved color depth detection model is proposed for information guided and multi feature fusion, and an absorption Markov model is introduced to optimize the guidance of low-level, middle-level, and high-level saliency maps, grasping different feature information contents. Subsequently, the gradual guidance of the network is achieved from aspects such as feature encoding, multi-scale and multi attention models, and attention refinement mechanisms. The experimental analysis of the fusion model proposed in the study showed that the average classification improvement accuracy of the fusion model reached 5.23%, and its error value was less than 0.1. The effectiveness on all four quantitative indicators exceeded 92%. The system’s detection response rate exceeded 93%, which is limited by the target object and results in a decrease in accuracy. This algorithm can provide reference value and means for target localization recognition and virtual scene detection.
Point cloud segmentation and recognition for virtual 3D wind turbine modeling in thermal plants are disjointed, limiting coverage and elevating mean square error. A design leveraging bilateral filtering of point cloud data for virtual 3D wind turbine modeling is proposed to address this. This approach encompasses data acquisition, preprocessing, and multi-level segmentation to enhance coverage. Bilateral filtering is then applied to calculate the 3D model, with edge correction and verification. Tests reveal that compared to traditional k-means clustering, reduced project space, and hybrid filtering methods, the proposed bilateral filtering algorithm significantly reduces mean square error, maintaining it below 10, demonstrating improved stability, safety, and practical value for thermal plant fan modeling.
