scholarly journals Personalized Financial News Recommendation Algorithm Based on Ontology

2015 ◽  
Vol 55 ◽  
pp. 843-851 ◽  
Author(s):  
Rui Ren ◽  
Lingling Zhang ◽  
Limeng Cui ◽  
Bo Deng ◽  
Yong Shi
Keyword(s):  
Recommendation Algorithm ◽  
Financial News ◽  
News Recommendation

scholarly journals Language models for financial news recommendation

2000 ◽  
Author(s):  
Victor Lavrenko ◽  
Matt Schmill ◽  
Dawn Lawrie ◽  
Paul Ogilvie ◽  
David Jensen ◽  
...  
Keyword(s):  
Language Models ◽  
Financial News ◽  
News Recommendation

scholarly journals Discovery News: A Generic Framework for Financial News Recommendation

2020 ◽  
Vol 34 (08) ◽  
pp. 13390-13395
Author(s):  
Chong Wang ◽  
Lisa Kim ◽  
Grace Bang ◽  
Himani Singh ◽  
Russell Kociuba ◽  
...  
Keyword(s):  
Recommender System ◽  
Financial Services ◽  
Review Process ◽  
Financial Analysis ◽  
Financial Data ◽  
Financial News ◽  
Generic Framework ◽  
Global Ratings ◽  
News Recommendation ◽  
Manual Review

In the financial services industry, it is crucial for analysts to constantly monitor and stay informed on the latest developments of their portfolio of companies. This ensures that analysts are up-to-date in their analysis and provide highly credible and timely insights. Currently, analysts receive news alerts through manually created news alert subscriptions that are often noisy and difficult to manage. The manual review process is time-consuming and error-prone. We demonstrate Discovery News, a framework for an automated news recommender system for financial analysis at S&P's Global Ratings. This system includes the automated ingestion, relevancy, clustering, and ranking of news. The proposed framework is adaptable to any form of input news data and can seamlessly integrate with other data used for analysis like financial data.

scholarly journals Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

Financial news recommendation based on graph embeddings

2019 ◽  
Vol 125 ◽  
pp. 113115 ◽  
Author(s):  
Jiangtao Ren ◽  
Jiawei Long ◽  
Zhikang Xu
Keyword(s):  
Graph Embeddings ◽  
Financial News ◽  
News Recommendation

Research on Recommendation Algorithm Based on Ranking Learning

2019 ◽  
Vol 17 (1) ◽  
pp. 60-73
Author(s):  
Xiaoli Zhang
Keyword(s):  
Learning To Rank ◽  
Dynamic Change ◽  
Optimization Criterion ◽  
Bayesian Optimization ◽  
Support Vector ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Overall Performance ◽  
News Recommendation ◽  
Better Than

After analyzing the logistic regression and support vector machine's limitation, the author has chosen the learning to rank method to solve the problem of news recommendations. The article proposes two news recommendation methods which were based on Bayesian optimization criterion and RankSVM. In addition, the article also proposes two methods to solve the dynamic change of user interest and recommendation novelty and diversity. The experimental results show that the two methods can get ideal results, and the overall performance of the method based on Bayesian optimization criterion is better than that based on RankSVM.

scholarly journals News recommendation system using machine learning

2018 ◽  
Vol 7 (2.18) ◽  
pp. 32
Author(s):  
Neha Rani ◽  
Sudhir Sudhir Pathak
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
High Accuracy ◽  
Svm Classifier ◽  
Financial News ◽  
The Neural Network ◽  
Market Forecast ◽  
News Recommendation ◽  
Weighted Values ◽  
The Given

The forecasting of financial news is yet becoming the main issue to divide the new into different classes on the basis of present time series. Moreover, it might be utilized for predicting and analyzing the stock market for the particular industry. Thus, the new content is significantly important to influence market forecast report. In this paper, the financial news from four countries namely America, Australia, India and South Africa along with their stop words are consider. The words along with their weighted values are determined and then the neural network is trained. Here, artificial neural network is used for classifying the appropriate results for the given input data. At last the comparison of ANN with SVM is shown. Experiments show that the ANN classification provides high accuracy to predict the news than the SVM classifier. 

Sign in / Sign up

Export Citation Format

Share Document