scholarly journals A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

2006 ◽  
pp. 234-243 ◽  
Author(s):  
Jiyong Zhang ◽  
Pearl Pu
Keyword(s):  
Comparative Study ◽  
Recommender Systems

A Comparative Study of Recommender Systems

2020 ◽  
pp. 1021-1029
Author(s):  
Yash Mehta ◽  
Aditya Singhania ◽  
Ayush Tyagi ◽  
Pranav Shrivastava ◽  
Mahesh Mali
Keyword(s):  
Comparative Study ◽  
Recommender Systems

Comparative study of recommender systems using movie rating data

2020 ◽  
Vol 31 (6) ◽  
pp. 975-991
Author(s):  
Seunghwan Lee ◽  
Youngsang Cho ◽  
Jun Seok Lee ◽  
Donghyeon Yu
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Rating Data

scholarly journals A comparative study of collaboration-based reputation models for social recommender systems

2013 ◽  
Vol 24 (3) ◽  
pp. 219-260 ◽  
Author(s):  
Kevin McNally ◽  
Michael P. O’Mahony ◽  
Barry Smyth
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Social Recommender Systems

scholarly journals Improvising Singular Value Decomposition by KNN for Use in Movie Recommender Systems

2019 ◽  
Vol 2 (1) ◽  
pp. 22-34
Author(s):  
Sukanya Patra ◽  
Boudhayan Ganguly
Keyword(s):  
Singular Value Decomposition ◽  
Comparative Study ◽  
Recommender Systems ◽  
Missing Values ◽  
Distance Measure ◽  
Study Data ◽  
Singular Value ◽  
Distance Measures ◽  
K Nearest Neighbors ◽  
Value Decomposition

Online recommender systems are an integral part of e-commerce. There are a plethora of algorithms following different approaches. However, most of the approaches except the singular value decomposition (SVD), do not provide any insight into the underlying patterns/concepts used in item rating. SVD used underlying features of movies but are computationally resource-heavy and performs poorly when there is data sparsity. In this article, we perform a comparative study among several pre-processing algorithms on SVD. In the experiments, we have used the MovieLens 1M dataset to compare the performance of these algorithms. KNN-based approach was used to find out K-nearest neighbors of users and their ratings were then used to impute the missing values. Experiments were conducted using different distance measures, such as Jaccard and Euclidian. We found that when the missing values were imputed using the mean of similar users and the distance measure was Euclidean, the KNN-based (K-Nearest Neighbour) approach of pre-processing the SVD was performing the best. Based on our comparative study, data managers can choose to employ the algorithm best suited for their business.

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