Singular value decomposition for collaborative filtering on a GPU

Abstract Recommender Systems (RS) help the users by showing better products and relevant items efficiently based on their likings and historical interactions with other users and items. Collaborative filtering is one of the most powerful technique of recommender system and provides personalized recommendation for users by prediction rating approach. Many Recommender Systems generally model only based on user implicit feedback, though it is too challenging to build RS. Conventional Collaborative Filtering (CF) techniques such as matrix decomposition, which is a linear combination of user rating for an item with latent features of user preferences, but have limited learning capacity. Additionally, it has been suffering from data sparsity and cold start problem due to insufficient data. In order to overcome these problems, an integration of conventional collaborative filtering with deep neural networks is proposed. A Weighted Parallel Deep Hybrid Collaborative Filtering based on Singular Value Decomposition (SVD) and Restricted Boltzmann Machine (RBM) is proposed for significant improvement. In this approach a user-item relationship matrix with explicit ratings is constructed. The user - item matrix is integrated to Singular Value Decomposition (SVD) that decomposes the matrix into the best lower rank approximation of the original matrix. Secondly the user-item matrix is embedded into deep neural network model called Restricted Boltzmann Machine (RBM) for learning latent features of user- item matrix to predict user preferences. Thus, the Weighted Parallel Deep Hybrid RS uses additional attributes of user - item matrix to alleviate the cold start problem. The proposed method is verified using two different movie lens datasets namely, MovieLens 100K and MovieLens of 1M and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate better prediction compared to other techniques in terms of accuracy.

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Movie Recommendation Engine using Collaborative Filtering with Alternative Least Square and Singular Value Decomposition Algorithms

IJARCCE ◽

10.17148/ijarcce.2019.8216 ◽

2019 ◽

Vol 8 (2) ◽

pp. 88-92

Author(s):

Rohan Mhetre ◽

Dr. Priya G

Keyword(s):

Singular Value Decomposition ◽

Collaborative Filtering ◽

Singular Value ◽

Least Square ◽

Decomposition Algorithms ◽

Movie Recommendation ◽

Value Decomposition

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Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500310 ◽

2021 ◽

pp. 1-19

Author(s):

Taushif Anwar ◽

V. Uma ◽

Gautam Srivastava

Keyword(s):

Singular Value Decomposition ◽

Collaborative Filtering ◽

Error Rate ◽

Recommendation System ◽

Recommendation Systems ◽

Matrix Decomposition ◽

Cold Start ◽

Singular Value ◽

Decomposition Approach ◽

Value Decomposition

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

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