scholarly journals Implementation and Customization of a Smart Mirror through a Facial Recognition Authentication and a Personalized News Recommendation Algorithm

2017 ◽  
Author(s):  
Ivette Cristina Araujo Garcia ◽  
Eduardo Rodrigo Linares Salmon ◽  
Rosario Villalta Riega ◽  
Alfredo Barrientos Padilla
Keyword(s):  
Facial Recognition ◽  
Recommendation Algorithm ◽  
News Recommendation

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.

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

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 A Hybrid News Recommendation Algorithm Based On K-means Clustering and Collaborative Filtering

2021 ◽  
Vol 1881 (3) ◽  
pp. 032050
Author(s):  
Jing Liu ◽  
Jinbao Song ◽  
Chen Li ◽  
Xiaoya Zhu ◽  
Ruyi Deng
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Algorithm ◽  
News Recommendation

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

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