scholarly journals Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

2020 ◽  
pp. 179-186
Author(s):  
Zainur Romadhon ◽  
Eko Sediyono ◽  
Catur Edi Widodo
Keyword(s):  
Collaborative Filtering ◽  
Fast Food ◽  
Recommendation System ◽  
Black Pepper ◽  
The Internet ◽  
Daily Lives ◽  
A Value ◽  
Similarity Calculation ◽  
Item Data ◽  
Filtering Approach

The Recommendation System plays an increasingly important role in our daily lives. With the increasing amount of information on the internet, the recommendation system can also solve problems caused by increasing information quickly. Collaborative filtering is one method in the recommendation system that makes recommendations by analyzing correlations between users. Collaborative filtering accumulates customer item ratings, identifies customers with common ratings, and offers recommendations based on inter-customer comparisons. This study aims to build a system that can provide recommendations to users who want to order or choose fast food menus. This recommendation system provides recommendations based on item data calculations with customer review data using a collaborative filtering approach. The results of applying cosine similarity calculation to determine fast food menu recommendations obtained for the item-based recommendation is Pizza Frankfurter BBQ Large with a value of 1.0, item-based with genre recommendation is Calblend Float with value 1.0 and user-based recommendation is Pizza Black Pepper Beef / Chicken Large with mean score 2.5.

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.

scholarly journals Utilization of CollaboratPemanfaatan Metode Based Collaborative Filtering Untuk Rekomendasi Wisata Di Kabupaten Malangive Filtering Method for Tourism Recommendations in Malang Regency

2019 ◽  
Vol 13 (2) ◽  
pp. 143
Author(s):  
Mufidatul Islamiyah ◽  
Puji Subekti ◽  
Titania Dwi Andini
Keyword(s):  
Collaborative Filtering ◽  
Predictive Value ◽  
Recommendation System ◽  
The Internet ◽  
Tourist Destination ◽  
Filtering Method ◽  
Manual Method ◽  
A Value ◽  
Benefit Level ◽  
Tourist Information

Tourist information in Malang district has not been widely spread on the internet, making it difficult for tourists to determine tourist destination in Malang district that will be visited. Therefore, this recommendation system was built to provide information about tourism in Malang district using the item based collaborative filtering method. The item based collaborative filtering method has the advantage of being able to increase the accuracy of the resulting object recommendations. Results from the questionnaire test that has been done, it is obtained that the tourism recommendation system is useful with the benefit level reaching 86.35% and in the item based collaborative filtering method test using the manual method in Excel and in the system produces the same predictive value namely prediction 1 with a value of 3.562, prediction 2 with a value of 3.287, and prediction 3 with a value of 1.896.

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

scholarly journals A Hybrid Ontology-Based Recommendation System in e-Commerce

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 239 ◽  
Author(s):  
Márcio Guia ◽  
Rodrigo Rocha Silva ◽  
Jorge Bernardino
Keyword(s):  
Collaborative Filtering ◽  
Experimental Evaluation ◽  
Recommendation System ◽  
Knowledge Engineering ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
The Internet ◽  
Active User ◽  
Expensive Process

The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.

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