scholarly journals Nearby Product Recommendation System Based on Users Rating

2019 ◽  
pp. 963-968 ◽  
Author(s):  
Akanksha Jyoti ◽  
Abhijeet Roy ◽  
Suraj Singh ◽  
Nabab Shaikh ◽  
Payal Desai
Keyword(s):  
Collaborative Filtering ◽  
Euclidean Distance ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Similarity Score ◽  
The Internet ◽  
K Nearest Neighbor ◽  
Past Behavior ◽  
Rate Score ◽  
K Nearest Neighbor Algorithm

The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.

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.

IDENTIFICATION OF HOAX BASED ON TEXT MINING USING K-NEAREST NEIGHBOR METHOD

2022 ◽  
Vol 10 (2) ◽  
pp. 217
Author(s):  
I Wayan Santiyasa ◽  
Gede Putra Aditya Brahmantha ◽  
I Wayan Supriana ◽  
I GA Gede Arya Kadyanan ◽  
I Ketut Gede Suhartana ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
The Internet ◽  
Test Results ◽  
K Nearest Neighbor ◽  
K Value ◽  
The Public ◽  
A Value ◽  
K Nearest Neighbor Algorithm ◽  
Time Information ◽  
Fold Cross Validation

At this time, information is very easy to obtain, information can spread quickly to all corners of society. However, the information that spreaded are not all true, there is false information or what is commonly called hoax which of course is also easily spread by the public, the public only thinks that all the information circulating on the internet is true. From every news published on the internet, it cannot be known directly that the news is a hoax or valid one. The test uses 740 random contents / issue data that has been verified by an institution, where 370 contents are hoaxes and 370 contents are valid. The test uses the K-Nearest Neighbor algorithm, before the classification process is performed, the preprocessing stage is performed first and uses the TF-IDF equation to get the weight of each feature, then classified using K-Nearest Neighbor and the test results is evaluated using 10-Fold Cross Validation. The test uses the k value with a value of 2 to 10. The optimal use of the k value in the implementation is obtained at a value of k = 4 with precision, recall, and F-Measure results of 0.764856, 0.757583, and 0.751944 respectively and an accuracy of 75.4%

scholarly journals An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2117
Author(s):  
Xuesheng Peng ◽  
Ruizhi Chen ◽  
Kegen Yu ◽  
Feng Ye ◽  
Weixing Xue
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Physical Distance ◽  
Spatial Distance ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

scholarly journals Result Prediction Using K-Nearest Neighbor Algorithm For Student Performance Improvement

2017 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Vijay Muralidharan ◽  
Pravien M. ◽  
Janani Balaji
Keyword(s):  
Student Performance ◽  
Performance Improvement ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Historical Data ◽  
Accurate Prediction ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
The University

Abstract—The examination results have become an integral part of every student’s life. The educational institution’s ranking is greatly influenced by the university results. This paper mainly focuses on the prediction of a student’s university result by making use of different attributes. These attributes might be of quantitative and qualitative type. The quantitative attributes used are Internal Assessments, Attendance percentage, Number of On-Duties taken and Overall Assignments completed. The qualitative attributes include Subject feedback, Faculty feedback, and whether the student is a Day Scholar/Hosteller. Here, we make use of k-Nearest Neighbor algorithm (or k-NN for short) against the historical data of students for more accurate prediction of results. In this method all the attributes considered are converted to the same scale. This algorithm makes use of the Euclidean distance formula which is used to find the nearest record. This algorithm predicts better results which help students maximize their academic output.

scholarly journals Finding Optimal Stations Using Euclidean Distance and Adjustable Surrounding Sphere

2021 ◽  
Vol 11 (2) ◽  
pp. 848
Author(s):  
Athita Onuean ◽  
Hanmin Jung ◽  
Krisana Chinnasarn
Keyword(s):  
Air Pollution ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Monitoring Network ◽  
K Nearest Neighbor ◽  
Rapid Urbanization ◽  
Spatial Coverage ◽  
K Nearest Neighbor Algorithm ◽  
Effective Network ◽  
Better Than

Air quality monitoring network (AQMN) plays an important role in air pollution management. However, setting up an initial network in a city often lacks necessary information such as historical pollution and geographical data, which makes it challenging to establish an effective network. Meanwhile, cities with an existing one do not adequately represent spatial coverage of air pollution issues or face rapid urbanization where additional stations are needed. To resolve the two cases, we propose four methods for finding stations and constructing a network using Euclidean distance and the k-nearest neighbor algorithm, consisting of Euclidean Distance (ED), Fixed Surrounding Sphere (FSS), Euclidean Distance + Fixed Surrounding Sphere (ED + FSS), and Euclidean Distance + Adjustable Surrounding Sphere (ED + ASS). We introduce and apply a coverage percentage and weighted coverage degree for evaluating the results from our proposed methods. Our experiment result shows that ED + ASS is better than other methods for finding stations to enhance spatial coverage. In the case of setting up the initial networks, coverage percentages are improved up to 22%, 37%, and 56% compared with the existing network, and adding a station in the existing one improved up by 34%, 130%, and 39%, in Sejong, Bonn, and Bangkok cities, respectively. Our method depicts acceptable results and will be implemented as a guide for establishing a new network and can be a tool for improving spatial coverage of the existing network for future expansions in air monitoring.

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