Music Recommendation and Discovery

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Tutorial on Music Recommendation by Oscar Celma (Gracenote) and Paul Lamere (The Echo Nest).The world of music is changing rapidly. We are now just a few clicks away from being able to listen to nearly any song that has ever been recorded. This easy access to a nearly endless supply of music is changing how we explore, discover, share and experience music.As the world of online music grows, music recommendation and discovery tools become an increasingly important way for music listeners to engage with music. Commercial recommenders such as Last.fm, iTunes Genius and Pandora have enjoyed commercial and critical success. But how well do these systems really work? How good are the recommendations? How far into the "long tail" do these recommenders reach? In this tutorial we look at the current state-of-the-art in music recommendation and discovery. We examine current commercial and research systems, focusing on the advantages and the disadvantages of the various recommendation strategies. We look at some of the challenges in building music recommenders and we explore some of the novel techniques that are being used to improve future music recommendation and discovery systems.Òscar Celma is the Chief Innovation Officer at Barcelona Music and Audio Technologies (BMAT). In 2008, Òscar obtained his Ph.D. in Computer Science and Digital Communication, in the Pompeu Fabra University (Barcelona, Spain). Òscar has a book published by Springer, titled "Music Recommendation and Discovery: The Long Tail, Long Fail and Long Play in the Music Digital Age" (2010). He holds 2 patents (US2003009344 and JP2003323188, 2002) from his work on the Vocaloid system, a singing voice-synthesizer bought by Yamaha in 2004. Follow on Twitter: @ocelmaPaul Lamere is the Director of Developer Platform for The Echo Nest, a music intelligence company located in Boston. Paul is interested in using technology to help people explore for new and interesting music. He is active in both the music information retrieval and the recommender systems research communities. Paul authors a popular blog on music technology at MusicMachinery.com. Follow on Twitter: @plamere

Transcript of Music Recommendation and Discovery

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Music Recommendation and Discovery Remastered

Tutorial

@recsys, 2011

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@plamere @ocelma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

How many songs fit in my pocket?

10 Songs1979

1,000 Songs2001

10,000,000 Songs2011

Music Recommendation is importantrecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

What's so special about music?● Huge item space● Very low cost per item● Many item types● Low consumption time● Very high per-item reuse● Highly passionate users ● Highly contextual usage● Consumed in sequences● Large personal collections● Doesn't require our full attention● Highly Social

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Music recommendation is broken ...If you like Britney Spears you might like...

...Report on Pre-War Intelligence

Let's look at some of the issues ....

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

What makes a good music recommendation?

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

What makes a good music recommendation?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevancerecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevance – cold start new or unpopular items

If you like Gregorian Chants you might like Green Day

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Cold Start – New User - Enrollmentrecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

New User – Implicit taste dataThe Audioscrobbler

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevance – Metadata Mismatches

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevance – Metadata Mismatches

Why?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevance - The grey sheep problem

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Relevance – Cultural Mismatches

What makes a good music recommendation?

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty and Serendipityrecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Popularity Bias - The Harry Potter Effect

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

...also known as the Coldplay effectrecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty / Serendipity – the enemy

High stakes competitions focused on relevance can reduce novelty and serendipity

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

What makes a good music recommendation?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

“If you like NiN you might like Johnny Cash” The Opacity Problem

Why???

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Is this a good recommendation?

If you like Norah Jones ...

You might like Ravi Shankar

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Is this a good recommendation?

If you like Norah Jones ...

You might like her father, Ravi Shankar

Photo cc by Mithrandir3

???????

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Brutal Death Metal Quiz

Brutal Death Metal Quiz

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Hacking the recommenderrecsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

What makes a good music recommendation?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

The limited reach of music recommendationHelp! I’m stuck in the head

Popu

lari

ty

Sales Rank83 Artists 6,659 Artists 239,798 Artists

0% ofrecommendations

48% of recommendations

Study by Dr. Oscar Celma - MTG UPF

52% of recommendations

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Personal discovery a challenge tooMusic Discovery Challenge

Listener StudyListeners 5,000

Average Songs Per User 3,500

Percent of songs never listened to

65%

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

● Relevance● Novelty / Serendipity● Transparency / Trust● Reach● Context

What makes a good music recommendation?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Music Recommendation is not just shopping

● It is not just for shopping, but...● Discovery● Exploration● Play ● Organization● Playlisting● Recommendation for groups● Devices

● Doesn't have to look like a spreadsheet!

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Context: Tools for exploration

http://techno.org/electronic-music-guide/

Ishkur's Guide to Electronic Dance Music

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Ingestion process

● Input data source● Own data, Customer, Labels, UGC, ...

● Protocol● Ingestion format

– TSV, XML, DDEX, XLS!, …● Method

– FTP, API, ...● Frequency

– Offline processing: Daily / weekly?– Data freshness!

● Documentation

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Ingestion process

● Post-processing● Data cleaning: Duplicates, normalization● Allow customer to use its own Ids!

● Add links to external sources● Rosetta Stone

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Ingestion process

● Considerations● Allow customer to use its own IDs when using the

rec. system.● How long does it take to process the whole

collection?● Incremental updates

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based● Content-based● Hybrid (combination)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based

“X similar to (or influenced by) Y”

Editorial metadata (Genre, Decades, Location, …)

Music Genome● Collaborative filtering● Social-based● Content-based● Hybrid (combination)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

Raw plays:

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

Raw plays:

Normalize to [5..1]

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

Probability distribution:0.32 , 0.24 , 0.18 , 0.1 , 0.07 , 0.04, 0.03 , 0.02

Normalize to [5..1]

Raw plays:

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

“people who listen to X also listen to Y”● Social-based● Content-based● Hybrid (combination)

Probability distribution:0.32 , 0.24 , 0.18 , 0.1 , 0.07 , 0.04, 0.03 , 0.02

Binary:1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1

Raw plays:

Normalize to [5..1]

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering

Matrix Factorization. E.g: SVD, NMF, ...● Social-based● Content-based● Hybrid (combination)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based

● WebMIR [Schedl, 2008]

● Content-based● Hybrid (combination)

Content Reviews Lyrics Blogs Social Tags Bios Playlists

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based● Content-based

“X and Y sound similar”● Hybrid

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based● Content-based

Audio features

– Bag-of-frames (MFCC) [Aucouturier, 2004], Rhythm [Gouyon, 2005], Harmony [Gomez, 2006], ...

Similarity

– KL-divergence: GMM [Aucouturier, 2002]– EMD [Logan, 2001]– Euclidean: PCA [Cano, 2005]– Cosine: mean/var (feature vectors)– Ad-hoc

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based● Content-based

Audio features

– Bag-of-frames (MFCC) [Aucouturier, 2004], Rhythm [Gouyon, 2005], Harmony [Gomez, 2006], ...

Similarity

– KL-divergence: GMM [Aucouturier, 2002]– EMD [Logan, 2001]– Euclidean: PCA [Cano, 2005]– Cosine: mean/var (feature vectors)– Ad-hoc

http://xkcd.com/26/

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

music recommendation approaches

● Expert-based● Collaborative filtering● Social-based● Content-based● Hybrid

Weighted (linear combination)– E.g CF * 0.2 + CT * 0.4 + CB * 0.4

Cascade– E.g 1st apply CF, then reorder by CT or CB

Switching

...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Search

● Metadata search

Bruce*

● Using filters: “Popular Irish bands from the 80s”

popularity:[8.0 TO 10.0] AND

iso_country:IE AND decade:1980

● Audio search (and similarity)● Query by example

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Similarity

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Similarity

Using Last.fm-360K dataset

? ? ?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Similarity

Using Last.fm-360K dataset

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Similarity' (include feedback)

Using Last.fm-360K dataset

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Beyond similarity

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Recommendation

● “If Paul likes Radiohead he might also like X”

vs.● “If Oscar likes Radiohead he might also like Y”

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Recommendation

● “If Paul likes Radiohead he might also like X”

vs.● “If Oscar likes Radiohead he might also like Y”

SIMILARITY != RECOMMENDATION

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Recommendation

● To whom are we recommending? Phoenix-2 (UK, 2006)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

lamere @ last.fm

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

mini-lamere's @ last.fm

● Clustering (k-means) lamere top-50 artists

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

mini-lamere's @ last.fm

● Clustering (k-means) lamere top-50 artists

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@lamere - Radiohead

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@lamere - Radiohead

Vs. Radiohead similar artists...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@lamere - Radiohead

Vs. Radiohead similar artists...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@lamere - Radiohead

Vs. Radiohead similar artists...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

@lamere - Radiohead

Vs. Radiohead similar artists...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Personalization (Itemization?)

● ...but also which Radiohead era?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Analytics

● Big data processing● capture, storage, search, share, analysis and

visualization● (local) Trend detection● Tastemakers● ...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Real-world Music Recommendation

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Real-world Music Recommendation

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Contextual Web Crawl

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Audio Processing

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Hybrid Recommendation

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

● 100 million registered users ● 37 million active monthly users● More than 900,000 songs in catalog● More than 90,000 artists in catalog● More than 11 billion thumbs● More than 1.9 billion stations● 95% of the collection was played in July 2011

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Curation and Analysis

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Weighting vectors

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

For unknown artists

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

For popular artists

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Country: UK

Record Labels: Acid Jazz, Sony BMG, Columbia

Genres: Funk, Disco, Acid Jazz, Jazz Fusion, Pop-rock

Years active: 1992 - present

Associated acts: Brand New Heavies, Guru, Julian Perretta

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Country: UK

Record Labels: Acid Jazz, Sony BMG, Columbia

Genres: Funk, Disco, Acid Jazz, Jazz Fusion, Pop-rock

Years active: 1992 - present

Associated acts: Brand New Heavies, Guru, Julian Perretta

Mood: upbeat, energetic

Rhythm: 120bpm, no rubato, high percusiveness

Key: Dm

Tags: acid jazz funk dance

Sounds like: Sereia (Tiefschwarz Radio Edit) by Mundo Azul

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

“I want some upbeat songs from unknown US bands, similar to Radiohead“

http://ella.bmat.ws/collections/bmat/artists/radiohead/similar/tracks ?filter=mood:happy +speed:fast +iso_country:US +popularity:[0.0+TO+4.0]

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Evaluation

"The key utility measure is user happiness. It seems reasonable to assume that relevance of the results is the most important factor: blindingly fast, useless answers do not make a user happy."

– "Introduction to Information Retrieval" (Manning, Raghavan, and Schutze, 2008)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

RMSE

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

RMSE?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

NO RMSE

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

NO RMSE (in music)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Evaluation

● Limitations of current metrics (RMSE, P/R, ROC, Spearman Rho, Kendall Tau, etc.)

● skewness– performed on test data that users chose to rate

● do not take into account– usefulness– novelty / serendipity– topology of the (item or user) similarity graph– ...

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Evaluation

If no RMSE then...?● Predictive Accuracy vs. Perceived Quality● Does the recommendation help the user? (user

satisfaction)● Familiarity vs. Novelty

● Does the recommendation help the system?● $$$● Catalog exposure

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

NEXT SONG?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

NEXT SONG?

?

Mean Reciprocal Rank+

User feedback

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

??

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

WTF?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

Emitt Rhodes

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

WHY as important as WHAT

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

WHY as important as WHAT

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

WHY as important as WHAT

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Novelty & Relevance

WHY as important as WHAT

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

WTF

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Other evaluation techniques

How can I evaluate a 3rd party recommender: objective measures:

coverage, reach

subject measures:Focus on precision

Measure irrelevant results: The WTF test

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

The WTF Test

Why the Freakomendation?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Evaluation

● Research Datasets● Million Song Dataset (CB, Social, Lyrics, Tags and

more)

http://labrosa.ee.columbia.edu/millionsong/

● Last.fm (CF)

http://ocelma.net/MusicRecommendationDataset/ – Last.fm 360K users <user, artist, total plays>– Last.fm 1K users <user, timestamp, artist, song>

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Monitoring

● Do not monitor (or test) only the Algorithm, but the WHOLE recommender system: KPIs

● Catalog● % matches against full catalog?● Ingestion time?● Availability?

● Data & Algorithms● Time computing (e.g. Matrix factorization)?● Matrix size (e.g. ~10M x ~1M) in memory?

– 10M vectors with 300 floats per vector → ~11Gb

● Time computing vector similarity O(n)?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Monitoring

USAGE● Search

assert_equal(ID(search('The The')), ID('The The'))

● Similarity

assert(similarity(U2, REM) > 0.8)

assert(similarity(AC/DC, Rebecca Black) < 0.3)

● Recommendation

0) create_profile(@ocelma)

1) assert(similarity(@ocelma, U2) >= 0.8)

2) dislike(@ocelma, track(U2,Lemon))

3) assert(similarity(@ocelma, album(U2,Zooropa)) < 0.8)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Monitoring

● (web) API● Measure query response

– Jmeter, Apache Benchmark● Process real logs

– Fake (repeated) queries → fast because using cache?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Monitoring

● (web) API● Measure query response

– Jmeter, Apache Benchmark● Process real logs

– Fake (repeated) queries → fast because using cache?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

INTRO CHORUS VERSE BRIDGE OUTRO

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Conclusions

● Music Recsys is multidisciplinary● search and filtering, musicology, data mining,

machine learning, personalization, social networks, text processing, complex networks, user interaction, information visualization, and signal processing (among others!)

● Music Recsys is important● These technologies will be integral in helping the next

generation of music listeners find that next favorite song

● Strong industry impact● Music Recsys is special

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Further research

● How well do music recommenders work?● lack of standardized data sets and objective

evaluation methods

● How to recognize and incorporate context into recommendations? ● listener’s context (exercising, exploring, working,

driving, relaxing, and so on)

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Further research

● How to make recommendations for all music? ● consider all music including new, unknown, and

unpopular content.

● What effect will automatic music recommenders have on the collective music taste?

recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma

Music Recommendation and Discovery Remastered

Tutorial

@recsys, 2011

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