Recommender System Using K-Nearest Neighbors and Singular Value Decomposition Algorithms: A Hybrid Approach

2020 ◽  
pp. 497-504
Author(s):  
Rounick Palit ◽  
Rajdeep Chatterjee
Keyword(s):  
Singular Value Decomposition ◽  
Recommender System ◽  
Hybrid Approach ◽  
Nearest Neighbors ◽  
Singular Value ◽  
Decomposition Algorithms ◽  
K Nearest Neighbors ◽  
Value Decomposition

scholarly journals Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

2021 ◽  
Vol 11 (6) ◽  
pp. 5541
Author(s):  
Badr Hssina ◽  
Abdelkader Grota ◽  
Mohammed Erritali
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Hybrid Approach ◽  
Singular Value ◽  
User Preferences ◽  
Decomposition Algorithms ◽  
K Nearest Neighbors ◽  
The Matrix ◽  
Value Decomposition

<span>Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.</span>

Extracting Pseudo Wave Fields from Transient Electromagnetic Fields Using a Weighting Coefficients Approach

2019 ◽  
Vol 24 (3) ◽  
pp. 351-359
Author(s):  
Junjie Xue ◽  
Jiulong Cheng ◽  
Guoqiang Xue ◽  
Hai Li ◽  
Dongyang Hou ◽  
...  
Keyword(s):  
Electromagnetic Field ◽  
Singular Value Decomposition ◽  
Early Time ◽  
Singular Value ◽  
Late Time ◽  
Decomposition Algorithms ◽  
Transient Electromagnetic ◽  
L2 Norm ◽  
Value Decomposition ◽  
Weighting Coefficients

The diffusive electromagnetic field can be transformed into the wave domain by means of mathematical conversion. The transformed field can then be interpreted with the tools in seismic data processing so that the identification to the underground targets can be effectively improved. However, the conversion is typically an ill-posed problem that needs to be solved using regularization tools. Based on the conventional regularization with smooth constraints in the L2 norm, the inversion result is of low resolution, while that obtained using truncated singular value decomposition (TSVD) methods is typically accurate, but has poor stability. To obtain a stable and accurate transformed electromagnetic field value, this study proposed to combine conventional regularization tools and singular value decomposition algorithms by incorporating a set of weighting coefficients. The proposed method is validated on both synthetic and observed data. The results from the proposed method are more accurate at the early time, and at the late time are more stable compared with the other methods. Furthermore, the example of field data shows that the proposed method could potentially further improve the interpretation accuracy of future mining explorations.

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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