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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