scholarly journals RANCANG BANGUN APLIKASI KURSUS ONLINE BERBASIS WEB DENGAN SISTEM REKOMENDASI METODE CONTENT-BASED FILTERING

2022 ◽  
Vol 7 (1) ◽  
pp. 23-36
Author(s):  
Yefta Christian ◽  
Kelvin Kelvin
Keyword(s):  
Cosine Similarity ◽  
Content Based Filtering

Lembaga Kursus dan Pelatihan (LKP) merupakan pendidikan non-formal yang menyediakan berbagai pelatihan khusus untuk mendidik keterampilan pelajar. Namun, terdapat hal yang sering menjadi kendala bagi calon pelajar yaitu bingung melakukan dalam melakukan pemilihan kursus sesuai dengan preferensinya. Oleh karena itu, tujuan dari penelitian ini adalah merancang sebuah aplikasi kursus online dengan sistem rekomendasi metode Content-based Filtering. Selain itu, penelitian ini juga menerapkan algoritma cosine similarity untuk menentukan kursus yang serupa dengan riwayat kursus yang pernah diakses oleh pengguna. Dataset yang digunakan untuk menjalankan algoritma sistem rekomendasi diambil dari situs Kaggle berjudul “Udemy Course” dengan jumlah data sebanyak 3,682 records. Hasil akhir dari penelitian ini adalah sebuah aplikasi kursus online yang dapat memberikan rekomendasi kursus berdasarkan nilai kesamaan yang paling tinggi dari algoritma cosine similarity.

scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

Product Recommendation System Design Using Cosine Similarity and Content-based Filtering Methods

2019 ◽  
Vol 3 (2) ◽  
pp. 42
Author(s):  
Cut Fiarni ◽  
Herastia Maharani
Keyword(s):  
System Design ◽  
Recommendation System ◽  
New Products ◽  
New Product ◽  
Similarity Score ◽  
Cosine Similarity ◽  
A Company ◽  
Content Based Filtering ◽  
Similarity Method ◽  
Suitable Product

The wide variety of products offered by a company, combined with the consistent demands of specific products from customers, create a certain problem for the organization when they want to market a new product. Organization need information that could help them promote the most suitable product based on their customer’s characteristics. The organization also need to suggest alternative products for customer if the requested product is unavailable. In this research, we design a Recommender System that could suggest either new or alternatif products to customer based on their characteristic and transaction history. This proposed system adopts Cosine Similarity method to calculate product similarity score and Content-based Filtering to calculate customer recommendation score and used as a model for the proposed system. Subsequently, these models are used to classify customers as well as products according to their transaction behavior and consequently recommends new products more likely to be purchased by them. Based on the testing results of the proposed system, it can be concluded that the chosen methods can be utilized to recommend products and costumer of products. It is shown that Precision and Recall of product similarity scores and customer recommendation for product scores are 100% and 93.47%.

scholarly journals Movie Recommending System Using Collaborative Filtering

2021 ◽  
Vol 9 (VII) ◽  
pp. 1034-1038
Author(s):  
P. Rama Rao
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cosine Similarity ◽  
Nearest Neighbours ◽  
And Performance ◽  
Content Based Filtering

Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of similar movies. In this paper, to prove the effectiveness of our system, K-NN algorithms and collaborative filtering are used. Here, the usage of cosine similarity is done for recommending the nearest neighbours.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

scholarly journals A novel Based-Approach Composed of Clustering Algorithm & Cosine Similarity for Products Recommendation

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Cosine Similarity ◽  
New Approach ◽  
System A ◽  
Weak Points ◽  
Cosine Similarity Measure ◽  
High Level ◽  
Content Based Filtering

There are huge tons of transactions being accomplished online every day. This implies that ecommerce is facing the problem of data and information overloads. While customers are shopping via websites, they spend a lot of time to search for the required products based on their needs. This problem can easily be alleviated by having an accurate recommendation system based on a strong algorithm and confident measures in this regard. There are two main techniques for products recommendation; content-based filtering and collaborative filtering. If one of these two techniques implemented on the e-commerce system, a lot of limitations and weak points will appear. This paper aims at generating an optimal list of product, which, in turn, generates an accurate and reliable list of items. The new approach is composed of three components; clustering algorithm, user-based collaborative filtering, and the Cosine similarity measure. This approach implemented using a real dataset of past experienced users. The accuracy of the search results is a matter to users, it recommends the most appropriate products to users of the e-commerce website. This approach shows trustworthy results and achieved a high level of accuracy for recommending products to users.

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