A Heuristic Video Recommendation Algorithm based on Similarity Computation for Multiple Features Analysis

2020 ◽  
Vol 13 ◽  
Author(s):  
Shuangyuan Li
Keyword(s):  
Large Scale ◽  
User Behavior ◽  
Recall Rate ◽  
Coverage Rate ◽  
Great Success ◽  
Recommendation Algorithm ◽  
Running Time ◽  
Cache Hit Ratio ◽  
Video Recommendation ◽  
Candidate Set

Objective:: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has increased greatly. In this way, mining and recommending videos for users based on their interests have become a very difficult problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multi-dimensional feature analysis and filtering is proposed. Methods:: Firstly, the video features are extracted from multiple dimensions such as user behavior and video tags. Then, the similarity analysis is carried out, and the video similarity degree is calculated by weighting, so as to obtain the similar video candidate set, and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results:: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion:: The proposed algorithm can effectively and greatly improve the accuracy, recall rate, coverage rate, running time and cache hit ratio.

scholarly journals Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

2020 ◽  
Author(s):  
Huibing Zhang ◽  
Junchao Dong
Keyword(s):  
Communication Network ◽  
Ensemble Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Recall Rate ◽  
Great Success ◽  
Mutual Benefit ◽  
Mobile Wireless ◽  
Evaluation Indexes ◽  
Wireless Communication Network

Abstract With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors were extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

scholarly journals Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Huibing Zhang ◽  
Junchao Dong
Keyword(s):  
Communication Network ◽  
Ensemble Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Recall Rate ◽  
Great Success ◽  
Mutual Benefit ◽  
Mobile Wireless ◽  
Evaluation Indexes ◽  
Wireless Communication Network

Abstract With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

scholarly journals Using Factor Decomposition Machine Learning Method to Music Recommendation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dapeng Sun
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
User Behavior ◽  
Frequent Pattern ◽  
User Preference ◽  
Data Conversion ◽  
Learning Method ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Candidate Set

The user data mining was introduced into the model construction process, and the user behavior was decomposed by analyzing various influencing factors through the factorization machine (FM) learning method. In the recommendation screening stage, the collaborative filtering recommendation is combined to screen the recommendation candidate set. The idea of user-based collaborative filtering (CF) is used for reference to obtain music works favored by similar users. On the other hand, we learn from item-based CF, which ensures that the candidate set covers user preference. Firstly, the user’s interest value is predicted by using dynamic interest model. Then, the common problems such as cold start and hot items processing are fully considered. The frequent pattern growth algorithm is compared with the association rule algorithm based on the collaborative filtering recommendation algorithm and the content-based recommendation algorithm, which proves the superiority of the algorithm and its role in solving the recommendation problem after applying the recommendation. The music data in the database data conversion effectively improve the efficiency and accuracy of mining. According to the implementation of the algorithm described in this article, the accuracy of the music recommendation results used to recommend user satisfaction is proved. And the recommended music is indeed feasible and practical.

scholarly journals TreeMerge: a new method for improving the scalability of species tree estimation methods

2019 ◽  
Vol 35 (14) ◽  
pp. i417-i426 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Large Scale ◽  
Species Tree ◽  
New Method ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Running Time ◽  
Tree Estimation ◽  
Computationally Intensive ◽  
A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

A Novel Stable Algorithm of Getting User’s Account Based on Telnet Protocol

2013 ◽  
Vol 299 ◽  
pp. 130-134
Author(s):  
Li Wei ◽  
Da Zhi Deng
Keyword(s):  
Network Security ◽  
Network Management ◽  
Large Scale ◽  
Computer Network ◽  
User Behavior ◽  
Political Stability ◽  
Future Internet ◽  
Stable Algorithm ◽  
Internet User Behavior ◽  
Security Incidents

In recent years,china input in the construction of the network management is constantly increasing;information technology has improved continuously,but,making a variety of network security incidents occur frequently,due to the vulnerability of the computer network system inherent,a direct impact on national security and social and political stability. Because of the popularity of computers and large-scale development of the Internet, network security has been increasing as the theme. Reasonable safeguards against violations of resources; regular Internet user behavior and so on has been the public's expectations of future Internet. This paper described a stable method of getting telnet user’s account in development of network management based on telnet protocol.

A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network

2021 ◽  
Author(s):  
Young Hyun Kim ◽  
Eun-Gyu Ha ◽  
Kug Jin Jeon ◽  
Chena Lee ◽  
Sang-Sun Han
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Large Scale ◽  
Oral Surgery ◽  
Human Identification ◽  
Running Time ◽  
Automated Method ◽  
Image Characteristics ◽  
Proposed Model

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) dataset. Methods: In total, 2,760 DPRs from 746 subjects who had 2 to 17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test dataset included the latest DPR of each subject (746 images) and the other DPRs (2,014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, –3, and −5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)–applied images. Results: This model had rank-1,–3, and −5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 sec per epoch, and the prediction time for 746 test DPRs was short (3.2 sec/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

Sign in / Sign up

Export Citation Format

Share Document