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: @ocelma
Paul 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
1. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Music Recommendation and Discovery
Remastered
Tutorial
@recsys, 2011
2. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@plamere @ocelma
3. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
INTRO CHORUS VERSE BRIDGE OUTRO
4. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
INTRO CHORUS VERSE BRIDGE OUTRO
5. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Music Recommendation is important
How many songs fit in my pocket?
10 Songs 1,000 Songs 10,000,000 Songs
1979 2001 2011
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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
7. 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 ....
8. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
9. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
10. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Relevance
11. recsys 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
12. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Cold Start – New User - Enrollment
13. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
New User – Implicit taste data
The Audioscrobbler
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Relevance – Metadata Mismatches
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Relevance – Metadata Mismatches
Why?
16. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Relevance - The grey sheep problem
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Relevance – Cultural Mismatches
18. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
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Novelty and Serendipity
20. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Popularity Bias - The Harry Potter Effect
21. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
...also known as the Coldplay effect
22. recsys 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
23. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
24. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
The Opacity Problem
“If you like NiN you might like Johnny Cash”
Why???
25. 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
26. 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
27. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Brutal Death Metal Quiz
???????
Photo cc by Mithrandir3
28. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Brutal Death Metal Quiz
29. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Hacking the recommender
30. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
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Help! I’m stuck in the head
The limited reach of music recommendation
48% of recommendations
Popularity
0% of
recommendations
52% of recommendations
83 Artists 6,659 Artists 239,798 Artists
Study by Dr. Oscar Celma - MTG UPF
Sales Rank
32. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Music Discovery Challenge
Personal discovery a challenge too
Listener Study
Listeners 5,000
Average Songs
3,500
Per User
Percent of
songs never 65%
listened to
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What makes a good music recommendation?
● Relevance
● Novelty / Serendipity
● Transparency / Trust
● Reach
● Context
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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!
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Context: Tools for exploration
Ishkur's Guide to Electronic Dance Music
http://techno.org/electronic-music-guide/
36. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
INTRO CHORUS VERSE BRIDGE OUTRO
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38. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
39. 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
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Ingestion process
● Post-processing
● Data cleaning: Duplicates, normalization
● Allow customer to use its own Ids!
● Add links to external sources
● Rosetta Stone
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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
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music recommendation approaches
● Expert-based
● Collaborative filtering
● Social-based
● Content-based
● Hybrid (combination)
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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)
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music recommendation approaches
● Expert-based
● Collaborative filtering
“people who listen to X also listen to Y”
● Social-based
● Content-based
● Hybrid (combination)
47. 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)
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music recommendation approaches
● Expert-based
● Collaborative filtering
“people who listen toRawalso listen to Y”
X plays:
● Social-based
● Content-based
● Hybrid (combination)
49. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
music recommendation approaches
● Expert-based
● Collaborative filtering
“people who listen toRawalso listen to Y”
X plays:
● Social-based Normalize to [5..1]
● Content-based
● Hybrid (combination)
50. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
music recommendation approaches
● Expert-based
● Collaborative filtering
“people who listen toRawalso listen to Y”
X plays:
● Social-based Normalize to [5..1]
Probability distribution:
● Content-based 0.32 , 0.24 , 0.18 , 0.1 , 0.07 , 0.04, 0.03 , 0.02
● Hybrid (combination)
51. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
music recommendation approaches
● Expert-based
● Collaborative filtering
“people who listen toRawalso listen to Y”
X plays:
● Social-based Normalize to [5..1]
Probability distribution:
● Content-based 0.32 , 0.24 , 0.18 , 0.1 , 0.07 , 0.04, 0.03 , 0.02
● Hybrid (combination) Binary:
100100000100000011000001
52. 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)
53. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
music recommendation approaches
● Expert-based
● Collaborative filtering
● Social-based
●
WebMIR [Schedl, 2008]
Content Reviews Lyrics Blogs Social Tags Bios Playlists
● Content-based
● Hybrid (combination)
54. 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
57. 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
...
58. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
59. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
60. 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
61. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Similarity
62. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Similarity
? ? ?
Using Last.fm-360K dataset
63. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Similarity
Using Last.fm-360K dataset
64. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Similarity' (include feedback)
Using Last.fm-360K dataset
65. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Beyond similarity
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Recommendation
● “If Paul likes Radiohead he might also like X”
vs.
● “If Oscar likes Radiohead he might also like Y”
67. 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
68. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Recommendation
● To whom are we recommending? Phoenix-2 (UK, 2006)
69. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
lamere @ last.fm
70. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
mini-lamere's @ last.fm
●
Clustering (k-means) lamere top-50 artists
71. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
mini-lamere's @ last.fm
●
Clustering (k-means) lamere top-50 artists
72. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@lamere - Radiohead
73. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@lamere - Radiohead
Vs. Radiohead similar artists...
74. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@lamere - Radiohead
Vs. Radiohead similar artists...
75. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@lamere - Radiohead
Vs. Radiohead similar artists...
76. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
@lamere - Radiohead
Vs. Radiohead similar artists...
77. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Personalization (Itemization?)
● ...but also which Radiohead era?
78. 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
● ...
79. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
INTRO CHORUS VERSE BRIDGE OUTRO
80. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Real-world Music Recommendation
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Real-world Music Recommendation
82. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Contextual Web Crawl
83. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Audio Processing
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Hybrid Recommendation
85. 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
86. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Curation and Analysis
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Weighting vectors
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For unknown artists
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For popular artists
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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
93. 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
94. 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]
95. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
INTRO CHORUS VERSE BRIDGE OUTRO
96. 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)
97. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
RMSE
98. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
RMSE?
99. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
NO RMSE
100. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
NO RMSE
(in music)
101. 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
– ...
102. 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
103. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
NEXT SONG?
104. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
NEXT SONG?
?
Mean Reciprocal Rank
+
User feedback
105. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
? ?
106. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
107. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
108. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
109. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
110. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
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Novelty & Relevance
WTF?
112. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
Emitt Rhodes
113. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
WHY as important as WHAT
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Novelty & Relevance
WHY as important as WHAT
115. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
WHY as important as WHAT
116. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
Novelty & Relevance
WHY as important as WHAT
117. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
WTF
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Other evaluation techniques
rd
How can I evaluate a 3 party recommender:
objective measures:
coverage, reach
subject measures:
Focus on precision
Measure irrelevant results: The WTF test
119. recsys 2011 | music recommendation and discovery tutorial | paul lamere & oscar celma
The WTF Test
Why the Freakomendation?
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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>
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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)?
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Monitoring
● (web) API
● Measure query response
– Jmeter, Apache Benchmark
● Process real logs
– Fake (repeated) queries → fast because using cache?
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Monitoring
● (web) API
● Measure query response
– Jmeter, Apache Benchmark
● Process real logs
– Fake (repeated) queries → fast because using cache?
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INTRO CHORUS VERSE BRIDGE OUTRO
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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
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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)
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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?
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Music Recommendation and Discovery
Remastered
Tutorial
@recsys, 2011