Comparing the Effectiveness of Query-Document Clusterings Using the QDSM and Cosine Similarity

2019 ◽  
Author(s):  
Claudio Gutierrez-Soto ◽  
Arturo Curiel Diaz ◽  
Gilles Hubert
Keyword(s):  
Cosine Similarity

Boundary Adjusted Network Based on Cosine Similarity for Temporal Action Proposal Generation

2021 ◽  
Author(s):  
Jingye Zheng ◽  
Dihu Chen ◽  
Haifeng Hu
Keyword(s):  
Cosine Similarity ◽  
Temporal Action

scholarly journals A Cosine Similarity Based Centralized Protection Scheme for DC Microgrids

2021 ◽  
pp. 1-1
Author(s):  
Rabindra Mohanty ◽  
Subham Sahoo ◽  
Ashok Kumar Pradhan ◽  
Frede Blaabjerg
Keyword(s):  
Cosine Similarity ◽  
Dc Microgrids ◽  
Protection Scheme

Multivariate Curve Resolution Combined with Estimation by Cosine Similarity Mapping of Analytical Data

The Analyst ◽  
2021 ◽  
Author(s):  
Yuya Nagai ◽  
Kenji Katayama
Keyword(s):  
Analytical Data ◽  
Cosine Similarity ◽  
Multivariate Curve Resolution ◽  
Curve Resolution

We developed a multivariate curve resolution (MCR) calculation combined with the mapping of cosine similarity (cos-s) for multiple mixture spectra of chemicals. The cos-s map was obtained by calculating the...

A cosine similarity-based negative selection algorithm for time series novelty detection

2006 ◽  
Vol 20 (6) ◽  
pp. 1461-1472 ◽  
Author(s):  
Yonggui Dong ◽  
Zhaoyan Sun ◽  
Huibo Jia
Keyword(s):  
Time Series ◽  
Negative Selection ◽  
Novelty Detection ◽  
Cosine Similarity ◽  
Selection Algorithm ◽  
Negative Selection Algorithm

Peringkas Teks Otomatis Berita Online Menggunakan Metode Cross Latent SemanticAnalysis & Cosine Similarity

2020 ◽  
Vol 9 (1) ◽  
pp. 105
Author(s):  
Muhammad Afif Ubaidillah ◽  
Ida Bagus Gede Dwidasmara ◽  
Agus Muliantara
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Cosine Similarity ◽  
F Measure

Ringkasan merupakan suatu cara yang efektif untuk meyajikan suatu karangan yang panjang dalam bentuk yang singkat. Walaupun bentuknya ringkas, namun ringkasan itu tetap memepertahankan pikiran pengarang dan pendekatannya yang asli. Namun dalam membuat ringkasan kita harus membaca berita atau artikel terlebih dahulu, sedangkan ringkasan dibuat dengan tujuan untuk meminimalkan waktu pembaca dan memberikan teks yang isinya langsung mengarah pada tujuan utama atau ide pokoknya. Pada penelitian ini memaparkan peringkasan teks otomatis berita online dari sebuah website menggunakan CLSA (Cross Latent Semantic Analysis) dan Cosine Similarity. Penelitian ini dilakukan untuk menguji seberapa baik hasil dan akurasi ringkasan yang dilakukan oleh CLSA dan cosine similarity. Penelitian ini menggunakan data sekunder dari berita dari media online yaitu web balipost.com dengan wilayah khusus Denpasar. Proses pengambilan data dilakukan dengan cara crawling. Data berita yang digunakan ialah sebanyak 161 berita, berita hasil ringkasan sistem nantinya akan dibandingkan dengan hasil ringkasan manual untuk mendapatkan akurasinya. Dari hasil pengujian yang dilakukan oleh sistem didapatkan nilai rata – rata akurasi F-Measure sebesar 58%, rata – rata Precision 62% dan rata – rata Recall 57%. Hasil dari penelitian peringkasan teks otomatis dari berita online dengan menggunakan metode CLSA dan cosine similarity memberikan hasil dan akurasi ringkasan yang cukup. Keywords : ringkasan, peringkas teks otomatis, crawling, CLSA, 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.

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