PACT: A New Trust Prediction Method for Multi-agents Recommender Systems

2017 ◽  
Author(s):  
Afef Selmi ◽  
Zaki Brahmi ◽  
Mohamed Mohsen Gammoudi
Keyword(s):  
Recommender Systems ◽  
Prediction Method ◽  
Trust Prediction ◽  
Multi Agents

A Review on User Recommendation System Based Upon Semantic Analysis

2017 ◽  
Vol 7 (11) ◽  
pp. 35
Author(s):  
Lovedeep Kaur ◽  
Naveen Kumari
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Pearson Correlation ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Support Vector ◽  
User Interest ◽  
Basic Algorithm ◽  
Abstract Class

Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

A Research on user Recommendation System Based upon Semantic Analysis

2017 ◽  
Vol 7 (11) ◽  
pp. 72
Author(s):  
Lovedeep Kaur ◽  
Naveen Kumari
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Pearson Correlation ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Support Vector ◽  
User Interest ◽  
Basic Algorithm ◽  
Abstract Class

Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

scholarly journals A Similarity-Inclusive Link Prediction Based Recommender System Approach

2019 ◽  
Vol 25 (6) ◽  
pp. 62-69 ◽  
Author(s):  
Zuhal Kurt ◽  
Kemal Ozkan ◽  
Alper Bilge ◽  
Omer Nezih Gerek
Keyword(s):  
Recommender Systems ◽  
Bipartite Graph ◽  
Recommender System ◽  
Link Prediction ◽  
Prediction Method ◽  
Research Field ◽  
Complex Domain ◽  
Proposed Model ◽  
Prediction Techniques ◽  
Item Item

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.

Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems

2019 ◽  
Vol 96 ◽  
pp. 324-335 ◽  
Author(s):  
Shunmei Meng ◽  
Lianyong Qi ◽  
Qianmu Li ◽  
Wenmin Lin ◽  
Xiaolong Xu ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Prediction Method ◽  
Privacy Preserving

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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