Information Filtering Based on Personalized Topology Information

2015 ◽  
Author(s):  
Bolun Chen ◽  
Ling Chen
Keyword(s):  
Information Filtering ◽  
Topology Information

scholarly journals Who Likes What Kind of News? The Relationship Between Characteristics of Media Consumers and News Interest

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110030
Author(s):  
Kai Kaspar ◽  
Lisa Anna Marie Fuchs
Keyword(s):  
Big Five ◽  
Information Filtering ◽  
Personal Characteristics ◽  
Self Esteem ◽  
Big Five Personality ◽  
Regression Analyses ◽  
Uses And Gratification ◽  
German Speaking ◽  
Explained Variance ◽  
The Relationship

Stimulated by the uses-and-gratification approach, this study examined the joint relation of several consumer characteristics to news interest. In total, 1,546 German-speaking participants rated their interest in 15 major news categories and several personal characteristics, including gender, age, the Big Five personality traits, self-esteem, as well as general positive and negative affect. Regression analyses examined the amount of interindividual variance in news interest that can be explained by this set of consumer characteristics. Overall, the amount of explained variance differed remarkably across news categories, ranging from 4% for entertainment-related news to 25% for news about technology. The most powerful explaining variables were participants’ gender, age, openness to experiences, and their amount of general positive affect. The results suggest that news interest should be defined and operationalized as a concept with multiple facets covering a huge range of content. Also, the results are important for media producers and journalists with respect to the conflict between increased need gratification of consumers and information filtering via personalized news content.

Topology detection of a distribution network based on adaptive state observer

2021 ◽  
pp. 1-9
Author(s):  
Yong Xiao ◽  
Yonggang Zeng ◽  
Yun Zhao ◽  
Yuxin Lu ◽  
Weibin Lin
Keyword(s):  
Smart Grid ◽  
Distribution Network ◽  
Detection Algorithm ◽  
State Observer ◽  
Matlab Simulation ◽  
Unknown Parameters ◽  
State Observation ◽  
Observation Network ◽  
Topology Information ◽  
Adaptive State Observer

The traditional distribution network lacks real-time topology information, which makes the implementation of smart grid complicated. The smart grid needs to monitor and dispatch the grid to maintain the economic and safe operation of the system. In this paper, we propose a topology detection algorithm of the distribution network based on adaptive state observer. Based on the transient dynamic model of the distribution network, the line states of the distribution network are regarded as unknown parameters, a virtual adaptive state observation network is built, and the topology can be inferred by the changes of adaptive state parameters. Finally, the effectiveness of our algorithm is verified by the MATLAB simulation experiments.

Trust and fuzzy inference based cross domain serendipitous item recommendations (TFCDSRS)

2021 ◽  
pp. 1-13
Author(s):  
Richa ◽  
Punam Bedi
Keyword(s):  
Fuzzy Inference ◽  
Mean Absolute Error ◽  
Information Filtering ◽  
Absolute Error ◽  
User Acceptance ◽  
Decision Making Process ◽  
End User ◽  
Cross Domain ◽  
F Measure ◽  
Filtering Approach

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals Recommender systems in model-driven engineering

2021 ◽  
Author(s):  
Lissette Almonte ◽  
Esther Guerra ◽  
Iván Cantador ◽  
Juan de Lara
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Research Work ◽  
Model Driven Engineering ◽  
Video Broadcasting ◽  
Model Driven ◽  
Challenges And Opportunities ◽  
Mapping Review ◽  
Development Processes ◽  
Systematic Mapping Review

AbstractRecommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

Information Filtering via Improved Similarity Definition

2010 ◽  
Vol 27 (6) ◽  
pp. 068903 ◽  
Author(s):  
Pan Xin ◽  
Deng Gui-Shi ◽  
Liu Jian-Guo
Keyword(s):  
Information Filtering

scholarly journals Gravity Effects on Information Filtering and Network Evolving

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91070 ◽  
Author(s):  
Jin-Hu Liu ◽  
Zi-Ke Zhang ◽  
Lingjiao Chen ◽  
Chuang Liu ◽  
Chengcheng Yang ◽  
...  
Keyword(s):  
Information Filtering ◽  
Gravity Effects
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