Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

2019 ◽  
Vol 11 (2) ◽  
pp. 1-13
Author(s):  
M. Sandeep Kumar ◽  
Prabhu J.
Keyword(s):  
Hybrid Model ◽  
Recommender System ◽  
Firefly Algorithm ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Research Work ◽  
Absolute Error ◽  
Experimental Result ◽  
Fuzzy C Means ◽  
Fcm Clustering

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals A Novel Rule based Data Mining Approach towards Movie Recommender System

2020 ◽  
Vol 44 (1) ◽  
pp. 157-170
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Phase 1 ◽  
Research Work ◽  
Rule Learning ◽  
Rule Base ◽  
Individual Member ◽  
Phase 2 ◽  
Group Recommendation ◽  
Rule Based

The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

scholarly journals Pembuatan Modul Rekomendasi pada OpenCart Menggunakan Metode Item-Based Collaborative Filtering

2013 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Kirana Nuryunita ◽  
Yani Nurhadryani
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Management System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Content Management ◽  
Cosine Similarity ◽  
Weighted Sum ◽  
Content Management System

<p>Penelitian ini bertujuan menambahkan modul rekomendasi pada content management system Opencart. Salah satu pendekatan dalam rekomendasi adalah item-based collaborative filtering. Metode item-based collaborative filtering dapat mengurangi waktu eksekusi perhitungan. Metode item-based collaborative filtering pada penelitian ini menggunakan perhitungan adjusted cosine similarity untuk menghitung nilai kemiripan antarbuku dan weighted sum untuk menghitung nilai prediksi rate buku. Untuk mendapatkan rekomendasi, pengguna harus melakukan login dan memberikan rate pada buku. Berdasarkan rate pengguna, nilai kemiripan dihitung menggunakan adjusted cosine similarity. Berdasarkan kemiripan antarbuku, nilai prediksi rate buku dicari menggunakan weighted sum. Sebelum buku direkomendasikan kepada pengguna, kategori prediksi buku dicocokkan dengan kategori buku yang telah diberi rate oleh pengguna. Penelitian ini menggunakan 300 buku dan 30 pengguna sebagai data. Dari hasil penelitian, hanya 17 pengguna yang mendapatkan rekomendasi. Pengujian dilakukan dengan menganalisis waktu eksekusi dan keakuratan rekomendasi. Waktu eksekusi dalam pengujian ini adalah 1.60 detik. Untuk menghitung keakuratan rekomendasi, penelitian ini menggunakan mean absolute error dengan hasil perhitungan 0.15.</p><p>Kata kunci: e-commerce, item-based collaborative filtering, recommender system.</p>

scholarly journals A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

2013 ◽  
pp. 157-165
Author(s):  
SONALI R. MAHAKALE ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Comparative Study ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Absolute Error ◽  
Image Filtering ◽  
Squared Error ◽  
The Comparative Study ◽  
Work Done

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time.

scholarly journals Product Recommendation System: A Systematic Literature Review

2021 ◽  
Vol 9 (VII) ◽  
pp. 3330-3339
Author(s):  
Ketki Kinkar
Keyword(s):  
Literature Review ◽  
Performance Measures ◽  
Systematic Literature Review ◽  
Recommender System ◽  
Recommendation System ◽  
Consumer Preferences ◽  
Research Work ◽  
Product Recommendation ◽  
System A ◽  
Scientific Papers

In today's world, we find a wide variety of search options and we may have difficulty selecting what we really need. The recommendation System plays an important part in dealing with these problems. A recommender system is a framework that is a filtering system that filters the data with various algorithms and recommends the user with the most relevant data. Recommendation systems are productive customization mechanisms, often up-to-date and recommendations based on current consumer preferences. These systems have shown to be extremely helpful in different areas of e-commerce, education, movies, music, books, films, scientific papers, and various products. This paper reviews many approaches of recommendation techniques with their upsides and downsides and diverse performance measures. We have reviewed various articles, analyzed their technique and approach, major features of the algorithm utilized, and potential areas for improvement in that research work.

scholarly journals An AI-Based Exercise Prescription Recommendation System

2021 ◽  
Vol 11 (6) ◽  
pp. 2661
Author(s):  
Hung-Kai Chen ◽  
Fueng-Ho Chen ◽  
Shien-Fong Lin
Keyword(s):  
Recommendation System ◽  
Exercise Prescription ◽  
Rest Heart Rate ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exercise Mode ◽  
Preventive Cardiology ◽  
Community Data ◽  
Testing Accuracy ◽  
Digital Training

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs.

scholarly journals Location Based Agricultural Product Recommendation System Using Novel KNN Algorithm

2019 ◽  
pp. 945-950
Author(s):  
Sachin J ◽  
Geethatharani P ◽  
Surya M K ◽  
Kavin K V
Keyword(s):  
Euclidean Distance ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Market Price ◽  
Absolute Error ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Product Recommendation ◽  
Use Of Data ◽  
Great Circle Distance

It is evident that the need for personalized product recommendation is much needed these days. Generally, product recommender systems are implemented in web servers that make use of data, implicitly obtained as results of the collection of Web browsing patterns of the users. Here, the project's motive is to provide location-based agricultural product recommendation system using a novel KNN algorithm by ensuring effective communication and transparency in agriculture trade marketing among buyers and sellers (farmers). It helps the farmer to fix up the market price by preventing the rue pricing of their products. The farmer can post their products into the application with price and other details like a timestamp of harvesting, color, size, the absence of pest, freshness, ripeness etc. Based on the location, the distance between the seller and buyer is calculated using great circle distance. An improved Novel KNN algorithm is used to find the K Nearest Seller by calculating the distance between the sellers and buyer using a Euclidean distance metric. The details posted by the farmers and buyers are stored and updated in a database dynamically. The recommender system recommends nearest sellers and their agricultural products based on buyer interest. The performance of the system is analyzed in terms of accuracy and mean absolute error.

scholarly journals Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF

2021 ◽  
Vol 9 (9) ◽  
pp. 181-191
Author(s):  
S. A. Azeem Farhan
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Value Decomposition

Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

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