scholarly journals Improving collaborative filtering using lexicon-based sentiment analysis

2022 ◽  
Vol 12 (2) ◽  
pp. 1744
Author(s):  
Rouhia Mohammed Sallam ◽  
Mahmoud Hussein ◽  
Hamdy M. Mousa
Keyword(s):  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Singular Value ◽  
The Internet ◽  
Second Phase ◽  
Mean Squared Errors ◽  
Analysis Phase ◽  
Two Phases ◽  
Value Decomposition

<span>Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.</span>

ALLEVIATING THE SPARSITY PROBLEM OF COLLABORATIVE FILTERING USING AN EFFICIENT ITERATIVE CLUSTERED PREDICTION TECHNIQUE

2012 ◽  
Vol 11 (01) ◽  
pp. 33-53 ◽  
Author(s):  
AMIRA ABDELWAHAB ◽  
HIROO SEKIYA ◽  
IKUO MATSUBA ◽  
YASUO HORIUCHI ◽  
SHINGO KUROIWA
Keyword(s):  
Collaborative Filtering ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Sparse Matrix ◽  
Hybrid Approach ◽  
Singular Value ◽  
Data Sets ◽  
Prediction Technique ◽  
Value Decomposition ◽  
Spectral Clustering Algorithm

Collaborative filtering (CF) is one of the most prevalent recommendation techniques, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. Although CF has been widely applied in various applications, its applicability is restricted due to the data sparsity, the data inadequateness of new users and new items (cold start problem), and the growth of both the number of users and items in the database (scalability problem). In this paper, we propose an efficient iterative clustered prediction technique to transform user-item sparse matrix to a dense one and overcome the scalability problem. In this technique, spectral clustering algorithm is utilized to optimize the neighborhood selection and group the data into users' and items' clusters. Then, both clustered user-based and clustered item-based approaches are aggregated to efficiently predict the unknown ratings. Our experiments on MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared to the hybrid user-based and item-based approach without clustering, hybrid approach with k-means and singular value decomposition (SVD)-based CF. Furthermore, we demonstrated the effectiveness of the proposed iterative technique and proved its performance through a varying number of iterations.

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

2010 ◽  
Vol 180 (22) ◽  
pp. 4290-4311 ◽  
Author(s):  
Ana Belén Barragáns-Martínez ◽  
Enrique Costa-Montenegro ◽  
Juan C. Burguillo ◽  
Marta Rey-López ◽  
Fernando A. Mikic-Fonte ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Singular Value ◽  
Value Decomposition ◽  
Filtering Approach

scholarly journals Design of a Hybrid Digital Watermarking Algorithm with High Robustness

2020 ◽  
Author(s):  
Yi Xie ◽  
Yulin Wang ◽  
Maode Ma
Keyword(s):  
Digital Watermarking ◽  
Singular Value ◽  
The Internet ◽  
Discrete Wavelet ◽  
Magic Square ◽  
Digital Multimedia ◽  
Security Scheme ◽  
Simulation Based ◽  
Simulation Results ◽  
Value Decomposition

With the development of the Internet, storage and transmission technologies such as printers and scanners, digital multimedia products are rapidly transmitted through the Internet broadcasting, multimedia works becoming easy to obtain and illegally tampering and copying. The copyright of media works urgently needs to be protected. As an important information security scheme, digital watermarking technology provides a powerful solution to the protection of multimedia works. In this paper, we propose an image digital watermarking algorithm combining discrete wavelet transform, discrete cosine transform and matrix singular value decomposition and new scrambling technique. Furthermore, to improve the robustness of the algorithm, grayscale scrambling and pseudo magic square transform are used. To evaluate our proposed algorithm, we realize the simulation based on Python 3.7.  All the simulation results show that our proposed algorithm has strong imperceptibility and robustness.    

scholarly journals A Hybrid Deep Collaborative Filtering Approach for Recommender Systems

2021 ◽  
Author(s):  
Kirubahari R ◽  
Miruna Joe Amali S
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Cold Start ◽  
Singular Value ◽  
User Preferences ◽  
Boltzmann Machine ◽  
Latent Features ◽  
Value Decomposition ◽  
Cold Start Problem

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.

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

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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