Movie Recommender System Based on Collaborative Filtering Using Apache Spark

Aljunid, Mohammed Fadhel; Manjaiah, D. H.

doi:10.1007/978-981-13-1274-8_22

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…This section illustrates previous work of different researchers with different breast cancer datasets.In [4] various machine learning classifiers like SVM classifier, Random Forest, KNN classifier and Decision Tree are compared with feature selection algorithm and results showed that Random Forest gave the best results with 93% accuracy. In [5] researchers compared different ML algorithms namely Naive Bayes, SVM, Decision TreeJ48, RandomForest, Bagging, AdaBoostandLogistic Regression over Wisconsin Breast Cancer dataset with PCA and results showed that Random forest gave the best results.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Gupta*¹,

Gill²

2020

IJITEE

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Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as timeconsuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy.In this paper, different machine learning and neural network algorithmhave been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared includeSupport Vector Machine Classification (SVM), K-Nearest NeighborClassification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm.

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Section: Literature Surveymentioning

confidence: 99%

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Gupta*¹,

Gill²

2020

IJITEE

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“…Some recent studies focused on speeding up the implementation of this algorithm [ 42 , 43 ]. Another study developed a recommender system for movies based on ALS using Apache Spark [ 44 ]. BPR is also a latent factor algorithm, but it is more appropriate for ranking a list of items.…”

Section: Introductionmentioning

confidence: 99%

Hybrid semantic recommender system for chemical compounds in large-scale datasets

2021

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The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of recommender systems to identify compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.

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“…Although a collaborative filtering algorithm is efficient and simple, it suffers from numerous problems such as cold start, prediction accuracy [17] and a shortage of capturing complex interactions between the user and the item [18]. Among the various collaborative filtering algorithms, the matrix decomposition (MD) [8, 19, 20] matches users and items into a common latent space using a vector of latent features showing a user or an item. Then, a user's interaction on an item got mapped as the dot product of their corresponding latent vectors.…”

Section: Introductionmentioning

confidence: 99%

“…The RSs have become an integral component in websites such as Google, Amazon, Netflix, YouTube and others [7–11]. RSs used many algorithms such as content‐based ones [9, 12], collaborative filtering [13] and trust‐based RS [14] algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi‐model deep learning approach for collaborative filtering recommendation system

Aljunid

Huchaiah

2020

CAAI trans. intell. technol.

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As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi‐model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one‐dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real‐world datasets, the proposed work exhibits substantial performance when compared to the existing methods.

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Movie Recommender System Based on Collaborative Filtering Using Apache Spark

Cited by 28 publications

References 10 publications

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Hybrid semantic recommender system for chemical compounds in large-scale datasets

Multi‐model deep learning approach for collaborative filtering recommendation system

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