scholarly journals Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30739-30749
Author(s):  
Zhan Su ◽  
Zuyi Lin ◽  
Jun Ai ◽  
Hui Li
Keyword(s):  
Recommender Systems ◽  
Complex Network ◽  
User Behavior ◽  
Network Modeling ◽  
Rating Prediction

Optimal Rating Prediction in Recommender Systems

2020 ◽  
pp. 331-339
Author(s):  
Bilal Ahmed ◽  
Li Wang ◽  
Waqar Hussain ◽  
M. Abdul Qadoos ◽  
Zheng Tingyi ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Rating Prediction

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

Review-Based Recommender Systems: A Proposed Rating Prediction Scheme Using Word Embedding Representation of Reviews

2020 ◽  
Author(s):  
S Hasanzadeh ◽  
S M Fakhrahmad ◽  
M Taheri
Keyword(s):  
Vector Space ◽  
Recommender Systems ◽  
Recommender System ◽  
Word Embedding ◽  
Bag Of Words ◽  
Prediction System ◽  
User Reviews ◽  
Prediction Scheme ◽  
Rating Prediction ◽  
The Given

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

scholarly journals (A337) State Failure as a Factor in International Global Medical Operations: Network Modeling

2011 ◽  
Vol 26 (S1) ◽  
pp. s95-s95
Author(s):  
A. Trufanov ◽  
A. Rossodivita ◽  
M. Aminova ◽  
A. Tikhomirov ◽  
A. Caruso ◽  
...  
Keyword(s):  
Complex Network ◽  
Good Governance ◽  
Network Modeling ◽  
Group Processes ◽  
Interdisciplinary Teams ◽  
National Society ◽  
Scale Free ◽  
Network Topologies ◽  
The Impact

IntroductionIn order to counteract disasters and emergencies, it is necessary to build cooperation and collaboration among all entities and actors. Field teams of rescuers require support from the State experiencing a disaster. The responses to the earthquake in Haiti demonstrated a lack of cooperation and collaboration and the rescuers encountered concomitant difficulties. Thus, the problems in the field are not only related to natural and technological aspects, but also social and political contexts. It is time to explore the role of the impact of State power on national and international disasters and emergencies. One modern and fruitful instrument for analysis of these complicated social and group processes is Complex Network modeling. Complex Network tools have been applied successfully to understanding and counteracting such threats as they relate to the spread of infectious diseases and/or to terrorist activities. Another significant utilization of the Complex Network approach is to develop good governance, management, and organizational processes in national and corporate landscapes.MethodsBased on a Complex Network Scope, a novel, three-layer network model of public connections for diverse State regimes for further simulation is proposed. Quantitative assessments and practical processes should be implemented for countering global disasters using international and interdisciplinary teams. Contrary to the known hierarchical layer approach for knowledge acquisition, this new model describes an overall national Society Network by dividing the approach into the three layers: (1) Formal (State), as hierarchical governments structures; (2) Informal (presented by different long-term sustainable link groups); and (3) Informal (aquatinters with short term links (“weak ties”).ResultsAccording to each of these layers, one of three types of network topologies exist: (1) hierarchical; (2) scale-free; and (3) random, respectively.

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