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CONTENT-BASED FILTERING FOR MUSIC RECOMMENDATION
Transcript of CONTENT-BASED FILTERING FOR MUSIC RECOMMENDATION
What is the problem ?
The current state of the music industry
Too many choices to choose from
Selecting the right type of music
Insufficient competence & skills
GrooveShark ~ 25 million songs
Google Play Music ~ 20 million songs
Spotify ~ 20 million songs
Explosive growth of digital music libraries
and music streaming services
Pandora Internet Radio
General pattern of the solutions
An agent/subsystem for analysing and selecting music items
What do they have in common?
Thank you for your attention!
Approaching the problem as:
Music listeners (consumers)
Focus on the implementation of the memory-based approach
Focus on the implementation of the model-based approach
Comparison between the two approaches
Focusing on the creation of a recommendation system for music items
The system should work in a content-based manner
Collaborative Filtering would not be considered for the scope of the project
External sources would be used for analyzing the dataset and extracting the acoustic attributes of the songs
Content-based recommendation systems
Collaborative Filtering systems
Differences between the two approaches
Each content-based system contains the following core components:
A content based recommendation system can be build by using either a memory-based and/or model-based approach.
Collaborative Filtering is the process of filtering or evaluating items using the opinions of other people
Schafer & Frankowski
Basic premises for Collaborative Filtering:
Items receive ratings
If users had similar taste in the past, they will have similar taste in the future as well
Collaborative Filtering systems can also be build using memory- and/or model-based approaches.
User Interface (UI)
Recommender System (RS)
K-Nearest Neighbours Algorithm
The system makes better recommendations when using higher k-NN values.
Naïve Bayesian Classifiers
"Preference Model Algorithm"
* This approach makes use of the acoustic attributes as well
The average results peak at 85% for α=0.15
None of the average values falls under 80%.
Comparison of approaches
Quality of recommendations
Project writing methodology
Did we manage to accomplish the goals set in the problem statement ?
What can be added or improved further?
Addition of a music player
Addition of a User Interface (UI)
Conducting additional tests with different setups
Addition of more features such environment,mood etc.
How successful is the implementation ?
Are we satisfied with the results ?