scholarly journals Personalized Recommendation via Suppressing Excessive Diffusion

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Guilin Chen ◽  
Xuzhen Zhu ◽  
Zhao Yang ◽  
Hui Tian
Keyword(s):  
Cross Validation ◽  
Information Overload ◽  
Recommendation Systems ◽  
Modern Society ◽  
Mass Diffusion ◽  
Personalized Recommendation ◽  
Similarity Estimation ◽  
Recommendation Algorithms ◽  
Benchmark Datasets ◽  
Fold Cross Validation

Efficient recommendation algorithms are fundamental to solve the problem of information overload in modern society. In physical dynamics, mass diffusion is a powerful tool to alleviate the long-standing problems of recommendation systems. However, popularity bias and redundant similarity have not been adequately studied in the literature, which are essentially caused by excessive diffusion and will lead to similarity estimation deviation and recommendation performance degradation. In this paper, we penalize the popular objects by appropriately dividing the popularity of objects and then leverage the second-order similarity to suppress excessive diffusion. Evaluation on three real benchmark datasets (MovieLens, Amazon, and RYM) by 10-fold cross-validation demonstrates that our method outperforms the mainstream baselines in accuracy, diversity, and novelty.

scholarly journals Design of Personalized Recommendation Algorithms based on Big Data Framework

2021 ◽  
Vol 2138 (1) ◽  
pp. 012025
Author(s):  
Fang Liu
Keyword(s):  
Big Data ◽  
Information Overload ◽  
Recommendation Systems ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Processing Technologies ◽  
Data Framework ◽  
Recommendation Algorithms ◽  
Data Environment ◽  
Better Than

Abstract The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms.

Personal Recommendation Via Heterogeneous Diffusion on Bipartite Network

2014 ◽  
Vol 23 (03) ◽  
pp. 1450003 ◽  
Author(s):  
Chunhua Ju ◽  
Chonghuan Xu
Keyword(s):  
Numerical Analysis ◽  
Information Overload ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Dynamic Feature ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Personal Recommendation ◽  
Benchmark Datasets

Recommender systems have proven to be an effective method to deal with the problem of information overload in finding interesting products. It is still a challenge to increase the accuracy and diversity of recommendation algorithms to fulfill users' preferences. To provide a better solution, in this paper, we propose a novel recommendation algorithm based on heterogeneous diffusion process on a user-object bipartite network. This algorithm generates personalized recommendation results on the basis of the physical dynamic feature of resources diffusion which is influenced by objects' degrees and users' interest degrees. Detailed numerical analysis on two benchmark datasets shows that the presented algorithm is of high accuracy, and also generates more diversity.

scholarly journals A Triangular Personalized Recommendation Algorithm for Improving Diversity

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Cai ◽  
Xiaowang Yang ◽  
Yusheng Huang ◽  
Hongjun Li ◽  
Qiang Sang
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
Online Databases ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Third Dimension ◽  
Triangular Area

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

scholarly journals Prediction Model for Potential Depression using Sex and Age-reflected qEEG Biomarkers.

2021 ◽  
Author(s):  
Taehyoung Kim ◽  
Ukeob Park ◽  
Seung Wan Kang
Keyword(s):  
Data Center ◽  
Cross Validation ◽  
Rating Scale ◽  
Modern Society ◽  
Classification Model ◽  
Test Accuracy ◽  
Z Score ◽  
Fold Cross Validation ◽  
Commit Suicide ◽  
Machine Learning Models

Abstract Depression is the mental disorder that prevalent in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests such as the Beck’s Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS) in conjunction with patient consultations. Traditional tests, however, are time consuming, can be trained on patients, and entail a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of QEEG indicators based on data from Data Center for Korean EEG with 116 potential depression subjects and 80 healthy controls. The classification model distinguished potential depression groups and normal groups with a test accuracy of up to 92.31% and a 10-fold cross validation loss of 0.13. This performance proposes a model with z-score QEEG metrics considering sex and age as an objective and a reliable method for detecting potential depression.

scholarly journals News Recommendation Systems in the Era of Information Overload

2021 ◽  
Author(s):  
Shuaishuai Feng ◽  
Junyan Meng ◽  
Jiaxing Zhang
Keyword(s):  
Information Overload ◽  
Recommendation Systems ◽  
Mobile Internet ◽  
Modern World ◽  
The Internet ◽  
The Public ◽  
Recommendation Algorithms ◽  
Cold Starts ◽  
News Recommendation ◽  
Source Of Information

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

Information filtering based on eliminating redundant diffusion and compensating balance

2019 ◽  
Vol 33 (13) ◽  
pp. 1950129 ◽  
Author(s):  
Xiangchun Liu ◽  
Xin Su ◽  
Jinming Ma ◽  
Yuxiao Zhu ◽  
Xuzhen Zhu ◽  
...  
Keyword(s):  
Heat Conduction ◽  
Statistical Physics ◽  
Information Filtering ◽  
Mass Diffusion ◽  
Similarity Estimation ◽  
Benchmark Datasets ◽  
Recommendation Accuracy ◽  
Novel Model ◽  
Improved Methods ◽  
Heat Conduction Process

In statistical physics, researchers concentrate on mass diffusion and heat conduction-based information filtering models, which effectively facilitate recommendation accuracy and diversity. There are many improved methods combining mass diffusion with heat conduction theories. Research results show that the best results are achieved when the combination of mass diffusion and heat conduction reaches equilibrium. With elaborative analysis, we find that similarity redundancies derive from the attribute correlations of objects, and deduce the similarity estimation deviation. Considering the former deficiencies, we propose a novel model through eliminating redundant diffusion and compensating balance (shortly ERD-CB), which symmetrically combines mass diffusion with heat conduction process through balance compensation. Three benchmark datasets from Movielens, Amazon and Netflix are used in our extensive experiments. Experiment results show that the ERD-CB model outperforms the benchmarkbaselines for accuracy, diversity and novelty.

scholarly journals Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fei Long
Keyword(s):  
Complex Systems ◽  
Information Overload ◽  
Rapid Development ◽  
Three Dimensional ◽  
Graph Model ◽  
Personalized Recommendation ◽  
Three Dimensional Model ◽  
Recommendation Algorithm ◽  
Tripartite Graph ◽  
Recommendation Algorithms

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

scholarly journals A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Min Gao ◽  
Bin Ling ◽  
Quan Yuan ◽  
Qingyu Xiong ◽  
Linda Yang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Information Overload ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Dbscan Clustering ◽  
Shilling Attack ◽  
Typical Item ◽  
The Common ◽  
Item Item

Personalized recommendation systems have been widely used as an effective way to deal with information overload. The common approach in the systems, item-based collaborative filtering (CF), has been identified to be vulnerable to “Shilling” attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first. The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-basedkNN (kNearest Neighbor) CF approach.

scholarly journals Measuring the Interestingness of News Articles

2011 ◽  
pp. 1194-1199
Author(s):  
Raymond K. Pon ◽  
Alfonso F. Cardenas ◽  
David J. Buttler
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Recommendation Systems ◽  
Online News ◽  
Personalized Recommendation ◽  
Government Officials ◽  
Definition Of ◽  
News Recommendation ◽  
Keyword Searches ◽  
User Personalization

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user (Carreira et al., 2004). Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article may not be interesting for various reasons, such as the article’s age or if it discusses an event that the user has already read about in other articles. Although it has been shown that collaborative filtering can aid in personalized recommendation systems (Wang et al., 2006), a large number of users is needed. In a limited user environment, such as a small group of analysts monitoring news events, collaborative filtering would be ineffective. The definition of what makes an article interesting – or its “interestingness” – varies from user to user and is continually evolving, calling for adaptable user personalization. Furthermore, due to the nature of news, most articles are uninteresting since many are similar or report events outside the scope of an individual’s concerns. There has been much work in news recommendation systems, but none have yet addressed the question of what makes an article interesting.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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