scholarly journals Modeling Trust for Recommender Systems using Similarity Metrics

2008 ◽  
pp. 103-118 ◽  
Author(s):  
Georgios Pitsilis ◽  
Lindsay F. Marshall
Keyword(s):  
Recommender Systems ◽  
Similarity Metrics

Similarity Metrics from Social Network Analysis for Content Recommender Systems

2016 ◽  
pp. 203-217 ◽  
Author(s):  
Guillermo Jimenez-Diaz ◽  
Pedro Pablo Gómez Martín ◽  
Marco Antonio Gómez Martín ◽  
Antonio A. Sánchez-Ruiz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Recommender Systems ◽  
Similarity Metrics

scholarly journals Recommender system for surplus stock clearance

2019 ◽  
Vol 9 (5) ◽  
pp. 3813
Author(s):  
Vipul Agarwal ◽  
Vijayalakshmi A
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
Similarity Metrics ◽  
Manual Work ◽  
Similarity Metric ◽  
Filtering Technique ◽  
Retail Shop ◽  
Item Item

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

Similarity metrics from social network analysis for content recommender systems

2017 ◽  
Vol 30 (3-4) ◽  
pp. 223-234 ◽  
Author(s):  
Guillermo Jimenez-Diaz ◽  
Pedro Pablo Gómez-Martín ◽  
Marco Antonio Gómez-Martín ◽  
Antonio A. Sánchez-Ruiz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Recommender Systems ◽  
Similarity Metrics

USE OF SIMILARITY METRICS IN TEMPLATE-BASED DETECTION OF OBJECTS IN IMAGES

2019 ◽  
Vol 78 (14) ◽  
pp. 1249-1261
Author(s):  
O. Rubel ◽  
S. K. Abramov ◽  
V. V. Abramova ◽  
V. V. Lukin
Keyword(s):  
Similarity Metrics ◽  
Detection Of Objects

Study and Comparative Analysis of Existing Recommender Systems

2019 ◽  
Vol 7 (1) ◽  
pp. 262-266
Author(s):  
Sanjay . ◽  
Yogesh Kumar ◽  
Rahul Rishi
Keyword(s):  
Comparative Analysis ◽  
Recommender Systems
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