Aiml ppt pdf.pdf on music recommendation system

Aiml model based project presentation

1. Artificial Intelligence and Machine Learning (21CS54)
“MUSIC RECOMMENDATION USING MACHINE
LEARNING”
Presented By :
Nayeem AfifShaikh (1BI21IS061)
Saanya Shukla (1BI21IS078)
UdhavGupta (1BI21IS102)
Akanksha Singh (1BI21IS131)
Bangalore Institute of Technology
Department of Information Science & Engineering
Faculty Incharge:
Dr. Roopa H
Associate Professor
Dept. of ISE, BIT

2. Contents
1.Problem Statement
2.Introduction
3.Objectives
4.Datasets
5.Techniques Used
6.Text Cleaning/Text Processsing
7.Feature Extraction
8.Similarity metrics
9.Recommendation Techniques
10.Serialization of data
11.Future Enhancements
13. Conclusion

3. Problem Statement
Developamachinelearningmodelformusicrecommendationleveragingtokenization
techniquestoprocessuserpreferencesefficiently.Theobjectiveistocreateasystem
thatanalyzesuserbehaviorandtokenizesmusicalfeatures,mitigatingsparsedata
challenges.Themodelmustnavigateissueslikeoverfittingandunderfitting,ensuring
adaptabilitytodiversemusicaltastesandgeneralizationtorecommendsongsacross
bothpopularandnichegenres.Employtokenizationtorepresentmusicalelementsas
inputfeatures,facilitatingeffectivelearningandpersonalizedplaylistgeneration.
Scalabilityiscrucialtoaccommodateanexpandingmusiclibrary,whilereal-time
responsivenessensuresanengaging,tailoredmusicdiscoveryexperienceforusers.

4. INTRODUCTION
•You love listening to music right? Imagine hearing your favorite song on any online music
platform let’s say Spotify.
•Suppose that the song’s finished, what now? Yes, the next song gets played automatically.
Have you ever imagined, how so? What is the logic or piece of code behind this?
•The same case might happen to you while watching a movie on Netflix or buying something
from Amazon. You get recommendations.
•Precisely, these recommendations are system generated and the logic behind them is nothing
but Machine Learning.
•Machine Learning is the ability to make machines learn and act.

5. •You search for some songs and listen to them and this is how the
machine learns. It then recommends songs to you on the basis of
various factors like singer or composer, movie, tone of the song,
whether it is romantic or disco, it is acoustic or original, etc.
•A recommendation system is a kind of filtering system which predicts
the preferences a user might give based on his/her activity. Machine
learning plays a super vital role in building these systems.
•This technique is widely popular and practiced in every streaming
platform like YouTube, Amazon, Netflix, Spotify, Tidal, etc. You don’t
have to play songs manually, instead, a music recommendation system
will do thatjobforyou.

6. •DevelopaRobustRecommendationModel:Implementamachinelearning
modelcapableofprovidingaccurateandpersonalizedmusicrecommendations
basedonuserpreferences.
•UtilizeAdvancedFeatureRepresentationTechniques:Applytokenizationand
otheradvancedfeaturerepresentationtechniquestocapturenuancedmusical
elements,enhancingthemodel'sabilitytounderstandandrecommenddiverse
musicgenres.
•IncorporateUserBehaviorAnalysis:Integrateuserbehavioranalysisintothe
modeltoensureadynamicandevolvingunderstandingofindividual
preferences,fosteringaccurateandreal-timerecommendations.
OBJECTIVES

7. DATASETS

9. TECHNIQUES USED
•Text Processing and NLP
•Feature Extraction
•Similarity Metrics
•Recommendation Techniques
•Serialization of data

10. TEXT CLEANING/TEXT PREPROCESSING
•Text cleaning in machine learning typically falls under the broader topic of "Data
Preprocessing" or "Data Cleaning." Data preprocessing is an essential step in the
machine learning pipeline, where raw data is transformed, cleaned, and formatted to
make it suitable for training machine learning models.
•Text cleaning is crucial for improving the quality of input data and reducing noise
that may negatively impact model performance. It helps in standardizing the text
representation and reducing the dimensionality of the feature space, making the data
more manageable for machine learning algorithms. Proper text cleaning enhances
the effectiveness of subsequent NLP tasks such as sentiment analysis, text
classification, named entity recognition, and topic modeling.

11. FEATURE EXTRACTION
•Featureextractioninamusicrecommendationsystemispivotalfortranslatingraw
musicdataintoaformatthatmachinelearningmodelscaneffectivelyprocess.
Variousstrategiesareemployedtoderivepertinentfeatures:
•MetadataFeaturesprovidesupplementaryinformationassociatedwithmusictracks,
encompassingartistdetails(genre,nationality,etc.),albuminformation(releaseyear,
recordlabel),trackduration,andtextualdatasuchassongtitlesandlyrics.
•CollaborativeFilteringFeaturesleverageuser-iteminteractiondata,comprisinguser
listeninghistories,preferences,playlists,anditempopularitymetrics,togenerate
personalizedrecommendationsbasedonuserbehaviourpatterns.

12. SIMILARITY METRICS
Inmusicrecommendationsystemsutilizingmachinelearning,similaritymetricsplayacrucial
roleindeterminingthelikenessbetweensongs,artists,orusers.
•CosineSimilarity:Thismetricmeasuresthecosineoftheanglebetweentwovectors,
representingthesimilarityintheirdirection.Inthecontextofmusicrecommendation,cosine
similaritycanbeappliedtocomparefeaturevectorsderivedfromaudiofeatures,metadata,
oruserpreferences.
•EuclideanDistance:Itcalculatesthestraight-linedistancebetweentwopointsinamulti-
dimensionalspace.Inmusicrecommendation,Euclideandistancecanquantifythe
dissimilaritybetweensongsorartistsbasedontheirfeaturerepresentations.

13. RECOMMENDATION TECHNIQUES
•Inmusicrecommendationsystemsleveragingmachinelearning,avarietyof
techniquesareemployedtogeneratepersonalizedrecommendationstailoredtothe
preferencesandbehaviorsofindividualusers.Thesetechniquesencompass
collaborativefiltering,content-basedfiltering,hybridapproaches,andsequential
recommendationmethods:
•CollaborativeFilteringtechniquesleveragethecollectivewisdomofuser
interactionstomakerecommendations.
•Content-BasedFilteringtechniquesfocusontheintrinsiccharacteristicsofmusic
itemsandusers'preferencestogeneraterecommendations.
•HybridRecommendationapproachescombinecollaborativefilteringandcontent-
basedfilteringtechniquestoexploitthestrengthsofbothmethods.
•SequentialRecommendationmethodsconsiderthetemporaldynamicsofuser
interactionsandrecommenditemsbasedonsequentialpatternsinuserbehavior.

14. SERIALIZATION OF DATA
•Inmusicrecommendationsystemspoweredbymachinelearning,serializationofdataplaysavital
roleinstoring,retrieving,andsharingtrainedmodels,aswellaspreservinguserpreferencesand
interactionhistories.Severaltechniquesarecommonlyemployedfordataserialization:
•ModelSerialization:Trainedmachinelearningmodelsareserializedtodiskinformatssuchas
pickle,JSON,orHDF5.
•FeatureSerialization:Extractedfeaturesfromaudiosignals,metadata,oruserinteractionsare
serializedtopreservetheirrepresentationsinacompactandportableformat.
•UserPreferencesSerialization:Userpreferences,suchaslisteninghistories,ratings,andplaylists,are
serializedtomaintainpersonalizedrecommendationsforindividualusers.

15. IMPLEMENTATION

19. OUTPUT

20. FUTURE ENHANCEMENT
•1.DeepLearningIntegration:-ExploreadvancedmodelslikeRNNs
ortransformersforintricatepatternrecognitioninuserbehaviorand
musiccontent.
•2.Context-Aware,Multi-ModalRecommendations-Enhance
recommendationsbyconsideringusercontext(activity,location)and
incorporatingaudio,lyrics,anduser-generatedcontentfeatures.
•3.ActiveLearningandPrivacy-PreservingTechniques:*-Implement
real-timeadaptationthroughactivelearningandprioritizeprivacy-
preservingmethodslikefederatedlearningforuserdataprotection.

21. CONCLUSION
Insummary,amusicrecommendationsystem,asubsetofmachinelearningalgorithms,
utilizesbigdatatosuggestmusicbasedonuserinterestsandbehavior.Employing
techniqueslikefactorization,vectorization,KNN,decisiontrees,andclustering,these
systemscanbecollaborative,content-based,hybrid,orknowledge-based.Intoday's
context,onlinemusicstreamingserviceshavesurpassedtraditionalrecordpurchasesin
popularityandeffectiveness.Theseservicesleveragerecommendationsystemstoenhance
customerexperience,providingaseamlessandprofitableinteraction.ForstudentsandML
enthusiasts,buildingamusicrecommendationsystemservesasachallengingyetinsightful
explorationintothepracticalapplicationsofmachinelearning,aligningacademic
knowledgewithindustrydemands.

22. Thank You !!