Dimension independent cosine similarity for collaborative filtering using MapReduce

2016 ◽  
Author(s):  
Fei Shen ◽  
Rachsuda Jiamthapthaksin
Keyword(s):  
Collaborative Filtering ◽  
Cosine Similarity

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

scholarly journals Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lili Wang ◽  
Ting Shi ◽  
Shijin Li
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recall Rate ◽  
Cosine Similarity ◽  
Fusion Method ◽  
Complex Matrix ◽  
Recommendation Algorithm ◽  
Kl Divergence ◽  
Experimental Effect ◽  
User Recommendation

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

scholarly journals Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering

2021 ◽  
pp. 202-208
Author(s):  
Daniel Theodorus ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
User Experience ◽  
Mean Squared Error ◽  
Cosine Similarity ◽  
Weighted Sum ◽  
Root Mean Squared Error ◽  
Squared Error

Industri 4.0 mendorong banyak perusahaan bertransformasi ke sistem digital. Machine Learning merupakan salah satu solusi dalam analisa data. Analisa data menjadi poin penting dalam memberikan layanan yang terbaik (user experience) kepada pelanggan. Lokasi yang diangkat dalam penelitian ini adalah PT. Sentral Tukang Indonesia yang bergerak dalam bidang penjualan bahan bangunan dan alat pertukangan seperti: cat, tripleks, aluminium, keramik, dan hpl. Dengan banyaknya data yang tersedia, menyebabkan perusahaan mengalami kesulitan dalam memberikan rekomendasi produk kepada pelanggan. Sistem rekomendasi muncul sebagai solusi dalam memberikan rekomendasi produk,  berdasarkan interaksi antara pelanggan dengan pelanggan lainnya yang terdapat di dalam data histori penjualan. Tujuan dari penelitian ini adalah Membantu perusahaan dalam memberikan rekomendasi produk sehingga dapat meningkatkan penjualan, memudahkan pelanggan untuk menemukan produk yang dibutuhkan, dan meningkatkan layanan yang terbaik kepada pelanggan.Data yang digunakan adalah data histori penjualan dalam 1 periode (Q1 2021), data pelanggan, dan data produk pada PT. Sentral Tukang Indonesia. Data histori penjualan tersebut akan dibagi menjadi 80% untuk dataset training dan 20% untuk dataset testing. Metode Item-based Collaborative Filtering pada penelitian ini memakai algoritma Cosine Similarity untuk menghitung tingkat kemiripan antar produk. Prediksi score memakai rumus Weighted Sum dan dalam menghitung tingkat error memakai rumus Root Mean Squared Error. Hasil dari penelitian ini memperlihatkan rekomendasi top 10 produk per pelanggan. Produk yang tampil merupakan produk yang memiliki score tertinggi dari pelanggan tersebut. Penelitian ini dapat menjadi referensi dan acuan bagi perusahaan dalam memberikan rekomendasi produk yang dibutuhkan oleh pelanggan.

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 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.

The Research on Collaborative Filtering in Personalization Recommendation System

2013 ◽  
Vol 846-847 ◽  
pp. 1137-1140
Author(s):  
Dan Han ◽  
Bing Liu ◽  
Yan Sun
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Performance Comparison ◽  
Cosine Similarity ◽  
A Performance

This paper does a performance comparison and evaluation to the CF algorithm based on the cosine similarity, the correlation similarity and project rating, and analyzes and researches its application, facing problems, solutions in the personalization recommendation system.

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 Hybrid Recommendation System for Video Games: Combining Content-based & Collaborative Filtering

2021 ◽  
Vol 9 (9) ◽  
pp. 1647-1653
Author(s):  
Aditya Manikantan
Keyword(s):  
Video Games ◽  
Collaborative Filtering ◽  
Video Game ◽  
Recommendation System ◽  
Data Representation ◽  
Cosine Similarity ◽  
Novel Approach ◽  
Hybrid Recommender ◽  
Implicit Data ◽  
Hybrid Recommendation

Abstract: Recommending video games can be trickier than movies. When it comes to selecting a video game, many factors are involved such as its genre, platform on which it’s played, duration of main and side quests, and more. However, recommending games based on just these features won’t suffice as a person who, for example, enjoys a certain genre of game can equally enjoy a vastly different genre. Therefore, a scoring mechanism is required which takes into account both, features of a game (contentbased filtering) and also studies the buying patterns of people playing a particular game (collaborative filtering). In this paper I have proposed a way to take into account both content-based and collaborative filtering into the final recommendation. I have used cosine similarity to quantify the similarity between the features of games. Along with this, I have employed a Deep fullyconnected AutoEncoder (DAE) to generalize the implicit data representation of an user’s buying patterns. Finally, I present a novel approach to combine the scores of these filtering techniques in such a way that it gives equal weightage to both. In other words, they both have equal influence over the final list of the top 10 games recommended to the user. Keywords: Hybrid Recommender, Collaborative filtering, Content-based filtering, Cosine similarity, AutoEncoder.

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 Recommender system for surplus stock clearance

2019 ◽  
Vol 9 (5) ◽  
pp. 3813
Author(s):  
Vipul Agarwal ◽  
Vijayalakshmi A
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
Similarity Metrics ◽  
Manual Work ◽  
Similarity Metric ◽  
Filtering Technique ◽  
Retail Shop ◽  
Item Item

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

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