A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Behera*, Dayal Kumar; ,; Das, Madhabananda; Swetanisha, Subhra; ,; ,

doi:10.35940/ijrte.d4362.118419

Cited by 9 publications

(2 citation statements)

References 42 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…r̅ u and r̅ v represents the average rating of user 'u' and user 'v', respectively. Rating of user 'u' for movie 'm' can be calculated using (5). (1)…”

Section: Methodsmentioning

confidence: 99%

“…Hybrid RS combines CF recommendation and content-based RS. The CF-based recommendation is applicable in many application domains [4], [5]. Model-based Collaborative filtering approach uses many machine learning [6] based models such as Random forest [7], support vector machine (SVM) [8], and matrix factorization [9] for predicting the users' likeness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

Behera

Das

Swetanisha

et al. 2021

IJEECS

View full text Add to dashboard Cite

<span>One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. This paper analyses the weighted hybrid CF system by integrating content K-nearest neighbors (KNN) with restricted Boltzmann machine (RBM). Movies are recommended to the active user in the proposed system by integrating the effects of both content-based and collaborative filtering. Model efficacy was tested with MovieLens benchmark datasets.</span>

show abstract

“…r̅ u and r̅ v represents the average rating of user 'u' and user 'v', respectively. Rating of user 'u' for movie 'm' can be calculated using (5). (1)…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

Behera

Das

Swetanisha

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

Custom Convolution Neural Network for Breast Cancer Detection

Prasanna,

S A

2023

IJEAT

View full text Add to dashboard Cite

Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN's architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the BreakHis dataset, which comprises biopsy slide images of patients in a range of cancer stages, the model is trained and verified. Comparing our findings to conventional techniques, we find notable gains in sensitivity, specificity, and accuracy. Gray-Level Co-Occurrence Matrix (GLCM) features extracted from the BreakHis dataset was used to analyze the performance on sequential neural network, transfer learning and machine learning models. After analysis, we have proposed hybrid models of CNN-SVM, CNN-KNN, CNN-Logistic regression and achieved accuracy of about 95.2%.

show abstract

Smart Artificial Intelligence System for Heart Disease Prediction

Nagaiah

2024

IJEAT

View full text Add to dashboard Cite

Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%

show abstract

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Cited by 9 publications

References 42 publications

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

Custom Convolution Neural Network for Breast Cancer Detection

Smart Artificial Intelligence System for Heart Disease Prediction

Contact Info

Product

Resources

About