Music Recommendation and Discovery in the Long Tail

Music Recommendation and Discovery in the Long Tail Òscar Celma Doctoral Thesis Defense (Music Technology Group ~ Universitat Pompeu Fabra)

PhD defense // UPF // Feb 16th 2009 Music Recommendation (personalized) and Discovery (explore large music collections) in the Long Tail (non-obvious, novel, relevant music)

PhD defense // UPF // Feb 16th 2009 “The Paradox of Choice: Why More Is Less”, Barry Schwartz (2004) The problem Paradox of choice

PhD defense // UPF // Feb 16th 2009 music overload • Today(August, 2007) iTunes: 6M tracks  P2P: 15B tracks  53% buy music on line  • Finding unknown, relevant music is hard! Awareness vs. access to content 

PhD defense // UPF // Feb 16th 2009 music overload? Digital Tracks – Sales data for 2007 ● ● Nearly 1 billion sold in 2007 ● ● 1% of tracks account for 80% of sales ● ● 3.6 million tracks sold less than 100 copies, and ● 1 million tracks sold exactly 1 copy ● • • •Data from Nielsen Soundscan 'State of the (US) industry' 2007 report

PhD defense // UPF // Feb 16th 2009 the Long Tail of popularity • Help me find it! [Anderson, 2006]

PhD defense // UPF // Feb 16th 2009 research questions • 1) How can we evaluate/compare different music recommendation approaches? • 2) How far into the Long Tail do music recommenders reach? • 3) How do users perceive novel (unknown to them), non-obvious recommendations?

PhD defense // UPF // Feb 16th 2009 If you like The Beatles you might like ...

PhD defense // UPF // Feb 16th 2009

PhD defense // UPF // Feb 16th 2009 • popularity bias • low novelty ratio

PhD defense // UPF // Feb 16th 2009 FACTORS AFFECTING RECOMMENDATIONS: Novelty Relevance Diversity Cold start Coverage Explainability Temporal effects

PhD defense // UPF // Feb 16th 2009 novelty vs. relevance

PhD defense // UPF // Feb 16th 2009 how can we measure novelty? • predictive accuracy vs. perceived quality • metrics MAE, RMSE, P/R/F-measure, ...  Test Train Can't measure novelty 

PhD defense // UPF // Feb 16th 2009 how can we measure novelty? • predictive accuracy vs. perceived quality • metrics MAE, RMSE, P/R/F-measure, ...  Can measure novelty 

PhD defense // UPF // Feb 16th 2009 how can we measure relevance? quot;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.quot;  quot;Introduction to Information Retrievalquot; (Manning, Raghavan, and Schutze, 2008)

PhD defense // UPF // Feb 16th 2009 research in music recommendation • Google Scholar Papers that contain “music recommendation” or “music recommender” in the title (Accessed October 1st, 2008)

PhD defense // UPF // Feb 16th 2009 research in music recommendation • ISMIR community

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering • Context-based • Content-based • Hybrid (combination)

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based AllMusicGuide  Pandora  • Collaborative filtering • Context-based • Content-based • Hybrid (combination)

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering User-Item matrix  [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001] • Context-based • Content-based

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering User-Item matrix  [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001] Similarity   Cosine  Adj. cosine  Pearson  SVD / NMF: matrix factorization • Context-based • Content-based

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering User-Item matrix  [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001] Similarity   Cosine  Adj. cosine  Pearson  SVD / NMF: matrix factorization Prediction (user-based)   Avg. weighted

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering • Context-based WebMIR  thrash [Schedl, 2008] Content Reviews Lyrics Blogs heavy metal Tags Bios Playlists Social Edgy Weird concert 90s Loud rock [Hu&Downie, 2006] [Celma et al., 2006] [Levy&Sandler, 2007] [Baccigalupo, 2008] [Symeonidis, 2008] • Content-based • Hybrid (combination)

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering • Context-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 • Hybrid (combination)

PhD defense // UPF // Feb 16th 2009 music recommendation approaches • Expert-based • Collaborative filtering • Context-based • Content-based • Hybrid (combination) Weighted  Cascade  Switching 

PhD defense // UPF // Feb 16th 2009 Work done

PhD defense // UPF // Feb 16th 2009 contributions

PhD defense // UPF // Feb 16th 2009 contributions 1) Network-based evaluation Item Popularity + Complex networks

PhD defense // UPF // Feb 16th 2009 contributions 1) Network-based evaluation Item Popularity + Complex networks 2) User-based evaluation

PhD defense // UPF // Feb 16th 2009 contributions 1) Network-based evaluation Item Popularity + Complex networks 2) User-based evaluation 3) Systems

PhD defense // UPF // Feb 16th 2009 contributions

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • 3 Artist similarity (directed) networks CF*: Social-based, incl. item-based CF (Last.fm)   “people who listen to X also listen to Y” CB: Content-based Audio similarity   “X and Y sound similar” EX: Human expert-based (AllMusicGuide)   “X similar to (or influenced by) Y”

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Small-world networks [Watts & Strogatz, 1998] Network traverse in a few clicks 

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation r = Pearson correlation  [Newman, 2002]

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation Kin(Bruce Springsteen)=534 => avg(Kin(sim(Bruce Springsteen)))=463

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation Kin(Bruce Springsteen)=534 => avg(Kin(sim(Bruce Springsteen)))=463 Kin(Mike Shupp)=14 => avg(Kin(sim(Mike Shupp)))=15

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation Kin(Bruce Springsteen)=534 => avg(Kin(sim(Bruce Springsteen)))=463 Kin(Mike Shupp)=14 => avg(Kin(sim(Mike Shupp)))=15 Homophily effect!

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Indegree – avg. neighbor indegree correlation Last.fm presents assortative mixing (homophily)   Artists with high indegree are connected together, and similarly for low indegree artists

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Last.fm is a scale-free network [Barabasi, 2000] power law exponent for the cumulative indegree  distribution [Clauset, 2007] A few artists (hubs) control the network 

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • Summary: artist similarity networks |------------|---------|-----|-----------| | | Last.fm | CB | Exp (AMG) | |------------|---------|-----|-----------| |Small World | yes | yes | yes | | | | | | |Ass. mixing | yes | No | No | | | | | | | Scale-free | yes | No | No | |------------|---------|-----|-----------| Last.fm artist similarity network resembles to a social  network (e.g. facebook)

PhD defense // UPF // Feb 16th 2009 complex network analysis :: artists • But, still some remaining questions... Are the hubs the most popular artists?  How can we navigate along the Long Tail, using  the artist similarity network?

PhD defense // UPF // Feb 16th 2009 contributions Long Tail analysis

PhD defense // UPF // Feb 16th 2009 the Long Tail in music • last.fm dataset (~260K artists)

PhD defense // UPF // Feb 16th 2009 the Long Tail in music • last.fm dataset (~260K artists) the beatles (50,422,827) radiohead (40,762,895) red hot chili peppers (37,564,100) muse (30,548,064) death cab for cutie (29,335,085) pink floyd (28,081,366) coldplay (27,120,352) metallica (25,749,442)

PhD defense // UPF // Feb 16th 2009 the Long Tail model [Kilkki, 2007] • F(x) = Cumulative distribution up to x

PhD defense // UPF // Feb 16th 2009 the Long Tail model [Kilkki, 2007] • Top-8 artists: F(8)~ 3.5% of total plays 50,422,827 the beatles 40,762,895 radiohead 37,564,100 red hot chili peppers 30,548,064 muse 29,335,085 death cab for cutie 28,081,366 pink floyd 27,120,352 coldplay 25,749,442 metallica

PhD defense // UPF // Feb 16th 2009 the Long Tail model [Kilkki, 2007] • Split the curve in three parts (82 artists) (6,573 artists) (~254K artists)

PhD defense // UPF // Feb 16th 2009 contributions + Long Tail analysis

PhD defense // UPF // Feb 16th 2009 artist indegree vs. artist popularity • Are the network hubs the most popular artists? ???

PhD defense // UPF // Feb 16th 2009 artist indegree vs. artist popularity Last.fm: correlation between Kin and playcounts   r = 0.621

PhD defense // UPF // Feb 16th 2009 artist indegree vs. artist popularity Audio CB similarity: no correlation   r = 0.032

PhD defense // UPF // Feb 16th 2009 artist indegree vs. artist popularity Expert: correlation between Kin and playcounts   r = 0.475

PhD defense // UPF // Feb 16th 2009 navigation along the Long Tail • “From Hits to Niches” # clicks to reach a Tail artist, starting in the Head  how many clicks?

PhD defense // UPF // Feb 16th 2009 navigation along the Long Tail • “From Hits to Niches” Audio CB similarity example (VIDEO) 

PhD defense // UPF // Feb 16th 2009 navigation along the Long Tail • “From Hits to Niches” Audio CB similarity example   Bruce Springsteen (14,433,411 plays)

PhD defense // UPF // Feb 16th 2009 navigation along the Long Tail • “From Hits to Niches” Audio CB similarity example   Bruce Springsteen (14,433,411 plays)  The Rolling Stones (27,720,169 plays)

PhD defense // UPF // Feb 16th 2009 navigation along the Long Tail • “From Hits to Niches” Audio CB similarity example   Bruce Springsteen (14,433,411 plays)  The Rolling Stones (27,720,169 plays)  Mike Shupp (577 plays)

PhD defense // UPF // Feb 16th 2009 artist similarity vs. artist popularity • navigation in the Long Tail Similar artists, given an artist in the HEAD part:  CF CB EXP 64,74% 60,92% 54,68% 45,32% 33,26% 28,80% (0%) 6,46% 5,82% Head Mid Tail Head Mid Tail Head Mid Tail Also, it can be seen as a Markovian Stochastic  process...

PhD defense // UPF // Feb 16th 2009 artist similarity vs. artist popularity • navigation in the Long Tail Markov transition matrix 

PhD defense // UPF // Feb 16th 2009 artist similarity vs. artist popularity • navigation in the Long Tail Last.fm Markov transition matrix 

PhD defense // UPF // Feb 16th 2009 artist similarity vs. artist popularity • navigation in the Long Tail From Head to Tail, with P(T|H) > 0.4  Number of clicks needed   CF : 5  CB : 2  EXP: 2 HEAD #clicks? TAIL

PhD defense // UPF // Feb 16th 2009 artist popularity Summary |-----------------------|---------|-----|-----------| | | Last.fm | CB | Exp (AMG) | |-----------------------|---------|-----|-----------| | Indegree / popularity| yes | no | yes | | | | | | |Similarity / popularity| yes | no | no | |-----------------------|---------|-----|-----------|

PhD defense // UPF // Feb 16th 2009 summary: complex networks+popularity |-----------------------|---------|-----|-----------| | | Last.fm | CB | Exp (AMG) | |-----------------------|---------|-----|-----------| | Small World | yes | yes | yes | | | | | | | Scale-free | yes | no | no | | | | | | | Ass. mixing | yes | no | no | |-----------------------|---------|-----|-----------| | Indegree / popularity| yes | no | yes | | | | | | |Similarity / popularity| yes | no | no | |-----------------------|---------|-----|-----------| | POPULARITY BIAS | YES | NO | FAIRLY | |-----------------------|---------|-----|-----------|

PhD defense // UPF // Feb 16th 2009 contribution #2: User-based evaluation • How do users perceive novel, non-obvious recommendations? Survey   288 participants Method: blind music recommendation   no metadata (artist name, song title)  only 30 sec. audio excerpt

PhD defense // UPF // Feb 16th 2009 music recommendation survey • 3 approaches: CF: Social-based Last.fm similar tracks  CB: Pure audio content-based similarity  HYbrid: AMG experts + audio CB to rerank songs   (Not a combination of the two previous approaches) • User profile: last.fm, top-10 artists  • Procedure Do you recognize the song?   Yes, Only Artist, Both Artist and Song title Do you like the song?   Rating: [1..5]

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • Overall results

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • Familiar recommendations (Artist & Song)

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • Ratings for novel recommendations

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • Ratings for novel recommendations one-way ANOVA within subjects (F=29.13, p<0.05)  Tukey's test (pairwise comparison) 

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • % of novel recommendations

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • % of novel recommendations one-way ANOVA within subjects (F=7,57, p<0.05)  Tukey's test (pairwise comparison) 

PhD defense // UPF // Feb 16th 2009 music recommendation survey: results • Novel recommendations Last.fm provides less % of novel songs, but of  higher quality

PhD defense // UPF // Feb 16th 2009 Why? besides better understanding of music recommendation... Open questions in the State of the Art in music discovery & recommendation: Is it possible to create a music discovery engine exploiting the music content in the WWW? How to build it? How can we describe the available music content? => SearchSounds Is it possible to recommend, filter and personalize music content available on the WWW? How to describe a user profile? What can we recommend beyond similar artists? => FOAFing the Music

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • Searchsounds Music search engine   keyword based search  “More like this” (audio CB)

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • Searchsounds Crawl MP3 blogs  > 400K songs analyzed 

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • Searchsounds Further work: improve song descriptions using   Auto-tagging [Lamere, 2008] [Turnbull, 2007] audio CB similarity [Sordo et al., 2007] tags from the text (music dictionary)  Feedback from the users thumbs-up/down tag audio content

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • FOAFing the music Music recommendation   constantly gathering music related info via RSS feeds  It offers: artist recommendation new music releases (iTunes, Amazon, eMusic, Rhapsody, Yahoo! Shopping) album reviews concerts close to user's locations related mp3 blogs and podcasts

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • FOAFing the music Integrates different user accounts (circa 2005!)  Semantic Web (FOAF, OWL/RDF) + Web 2.0  2nd prize Semantic Web Challenge (ISWC 2006) 

PhD defense // UPF // Feb 16th 2009 contribution #3: two complete systems • FOAFing the music Further work:   Follow Linking Open Data best practices  Link our music recommendation ontology with Music Ontology [Raimond et al., 2007]  (Automatically) add external information from: Myspace Jamendo Garageband ...

PhD defense // UPF // Feb 16th 2009 summary of contributions :: research questions • 1) How can we evaluate/compare different music recommendation approaches? • 2) How far into the Long Tail do music recommenders reach? • 3) How do users perceive novel (unknown to them), non-obvious recommendations?

PhD defense // UPF // Feb 16th 2009 summary of contributions :: research questions • 1) How can we evaluate/compare different music recommendation approaches? Objective framework comparing music rec.  approaches (CF, CB, EX) using Complex Network analysis Highlights fundamental differences among the  approaches • 2) How far into the Long Tail do music recommenders reach? • 3) How do users perceive novel (unknown to them), non-obvious recommendations?

PhD defense // UPF // Feb 16th 2009 summary of contributions :: research questions • 1) How can we evaluate/compare different music recommendation approaches? • 2) How far into the Long Tail do music recommenders reach? Combine 1) with the Long Tail model, and Markov  model theory Highlights differences in terms of discovery and  navigation • 3) How do users perceive novel (unknown to them), non-obvious recommendations?

PhD defense // UPF // Feb 16th 2009 summary of contributions :: research questions • 1) How can we evaluate/compare different music recommendation approaches? • 2) How far into the Long Tail do music recommenders reach? • 3) How do users perceive novel (unknown to them), non-obvious recommendations? Survey with 288 participants  Still room to improve novelty (3/5 or less...)   To appreciate novelty users need to understand the recommendations

PhD defense // UPF // Feb 16th 2009 summary of contributions :: research questions • 1) How can we evaluate/compare different music recommendation approaches? • 2) How far into the Long Tail do music recommenders reach? • 3) How do users perceive novel (unknown to them), non-obvious recommendations? => Systems that perform best (CF) do not exploit the  Long Tail, and Systems that can ease Long Tail navigation (CB) do  not perform good enough Combine (hybrid) different approaches! 

PhD defense // UPF // Feb 16th 2009 Systems that perform  best (CF) do not exploit the Long Tail, and Systems that can ease  Long Tail navigation (CB) do not perform good enough Combine different  approaches!

PhD defense // UPF // Feb 16th 2009 summary of contributions :: systems • Furthermore... 2 web systems that improved existing State of the  Art work in music discovery and recommendation  Searchsounds: music search engine exploiting music related content in the WWW  FOAFing the Music: music recommender based on a FOAF user profile, also offering a number of extra features to complement the recommendations

PhD defense // UPF // Feb 16th 2009 further work :: limitations • 1) How can we evaluate/compare different recommendations approaches? Dynamic networks  [Leskovec, 2008]  track item similarity over time  track user's taste over time  trend and hype detection

PhD defense // UPF // Feb 16th 2009 further work :: limitations • 2) How far into the Long Tail do recommendation algorithms reach? Intercollections  how to detect bad quality music in the tail? 

PhD defense // UPF // Feb 16th 2009 further work :: limitations • 3) How do users perceive novel, non-obvious recommendations?  User understanding [Jennings, 2007]  savant, enthusiast, casual, indifferent Transparent, steerable recommendations  [Lamere & Maillet, 2008]  Why? as important as What?

PhD defense // UPF // Feb 16th 2009 summary: articles • #1) Network-based evaluation for RS  O. Celma and P. Cano. “From hits to niches? or how popular artists can bias music recommendation and discovery”. ACM KDD, 2008.  J. Park, O. Celma, M. Koppenberger, P. Cano, and J. M. Buldu. “The social network of contemporary popular musicians”. Journal of Bifurcation and Chaos (IJBC), 17:2281–2288, 2007.  M. Zanin, P. Cano, J. M. Buldu, and O. Celma. “Complex networks in recommendation systems”. WSEAS, 2008  P. Cano, O. Celma, M. Koppenberger, and J. M. Buldu “Topology of music recommendation networks”. Journal Chaos (16), 2006. • #2) User-based evaluation for RS  O. Celma and P. Herrera. “A new approach to evaluating novel recommendations”. ACM RecSys, 2008.

PhD defense // UPF // Feb 16th 2009 summary: articles • #3) Prototypes FOAFing the Music   O. Celma and X. Serra. “FOAFing the music: Bridging the semantic gap in music recommendation”. Journal of Web Semantics, 6(4):250–256, 2008.  O. Celma. “FOAFing the music”. 2nd Prize Semantic Web Challenge ISWC, 2006.  O. Celma, M. Ramirez, and P. Herrera. “FOAFing the music: A music recommendation system based on rss feeds and user preferences”. ISMIR, 2005.  O. Celma, M. Ramirez, and P. Herrera. “Getting music recommendations and filtering newsfeeds from foaf descriptions”. Scripting for the Semantic Web, ESWC, 2005.

PhD defense // UPF // Feb 16th 2009 summary: articles • #3) Prototypes Searchsounds   O. Celma, P. Cano, and P. Herrera. “Search sounds: An audio crawler focused on weblogs”. ISMIR, 2006.  V. Sandvold, T. Aussenac, O. Celma, and P. Herrera. “Good vibrations: Music discovery through personal musical concepts”. ISMIR, 2006.  M. Sordo, C. Laurier, and O. Celma. “Annotating music collections: how content-based similarity helps to propagate labels”. ISMIR, 2007.

PhD defense // UPF // Feb 16th 2009 summary: articles • Misc. (mainly MM semantics)  R. Garcia C. Tsinaraki, O. Celma, and S. Christodoulakis. “Multimedia Content Description using Semantic Web Languages” book, Chapter 2. Springer–Verlag, 2008.  O. Celma and Y. Raimond. “Zempod: A semantic web approach to podcasting”. Journal of Web Semantics, 6(2):162–169, 2008.  S. Boll, T. Burger, O. Celma, C. Halaschek-Wiener, E. Mannens. “Multimedia vocabularies on the Semantic Web”. W3C Technical report, 2007.  O. Celma, P. Herrera, and X. Serra. “Bridging the music semantic gap”. SAMT, 2006.  R. Garcia and O. Celma. “Semantic integration and retrieval of multimedia metadata”. ESWC, 2005

PhD defense // UPF // Feb 16th 2009 summary: articles  R. Troncy, O. Celma, S. Little, R. Garcia and C. Tsinaraki. “MPEG-7 based multimedia ontologies: Interoperability support or interoperability issue?” MARESO, 2007.  M. Sordo, O. Celma, M. Blech, and E. Guaus. “The quest for musical genres: Do the experts and the wisdom of crowds agree?”. ISMIR, 2008. • Music Recommendation Tutorials -- with Paul Lamere  ACM MM, 2008 (Vancouver, Canada)  ISMIR, 2007 (Vienna, Austria)  MICAI, 2007 (Aguascalientes, Mexico)

PhD defense // UPF // Feb 16th 2009 summary: dissemination • PhD Webpage http://mtg.upf.edu/~ocelma/PhD   PDF  Source code Long Tail Model in R  References Citeulike  Related links delicious

PhD defense // UPF // Feb 16th 2009 acknowledgments NB: The complete list of acknowledgments can be found in the document

Music Recommendation and Discovery in the Long Tail Òscar Celma Doctoral Thesis Defense (Music Technology Group ~ Universitat Pompeu Fabra)

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Music Recommendation and Discovery in the Long Tail

by Oscar Celma on Feb 25, 2009

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Music Recommendation and Discovery in the Long Tail — Presentation Transcript