Context Awareness by Case-Based Reasoning in a Music Recommendation System

2007 ◽  
pp. 45-58 ◽  
Author(s):  
Jae Sik Lee ◽  
Jin Chun Lee
Keyword(s):  
Context Awareness ◽  
Recommendation System ◽  
Case Based Reasoning ◽  
Music Recommendation ◽  
Music Recommendation System ◽  
Case Based

Music Recommendation System Based on Context Using Case-Based Reasoning and Self Organizing Map

2016 ◽  
Vol 4 (2) ◽  
pp. 459
Author(s):  
Gst Ayu Vida Mastrika Giri ◽  
Agus Harjoko
Keyword(s):  
Recommendation System ◽  
Case Based Reasoning ◽  
Self Organizing Map ◽  
Music Recommendation ◽  
Indexing Method ◽  
System Output ◽  
Context Data ◽  
Music Recommendation System ◽  
Case Based ◽  
Self Organizing

Effective music recommendation can decrease listener’s effort in choosing music that will be listened. Music recommendation is not only can be obtained based on genre or audio similarity, because listener’s music choices are also influenced by the listener’s context (mood, occasion, part of day, date, weather, region, month, and weekday). This research used Case-Based Reasoning (CBR) for determining music recommendation based on listener’s context data and also Self Organizing Map (SOM) which is used as an indexing method in CBR. Inputs given by user to the system are user’s occasion and mood desired by user. The system output is a playlist consists of music that suitable with user’s context and desired mood.

scholarly journals Neural Collaborative For Music Recommendation System

2021 ◽  
Vol 1071 (1) ◽  
pp. 012021
Author(s):  
Abba Suganda Girsang ◽  
Antoni Wibowo ◽  
Jason ◽  
Roslynlia
Keyword(s):  
Recommendation System ◽  
Music Recommendation ◽  
Music Recommendation System

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.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

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