Dbrec - Music recommendations using DBpedia

dbrecMusic recommendations using DBpedia Alexandre Passant - DERI, NUI Galway In-Use Track @ ISWC2010 11th November 2010, Shanghai, China

Good news, it doesn’t fitanymore in a slide !Many producers, only afew consumers (besidessearch engines): BBC,Drupal ,,,

Agenda• Semantic Distance over Linked Data• dbrec - architecture, dataset and UI• Evaluation• Lessons learnt• Next steps and conclusion

Semantic Distance

Semantic Distance over Linked Data• Relying only on links• Relying only on instance data• Using dereferencable URIs • And using resources following the LD principles

Linked Data

Linked Data e:l1e:r1 e:l1 e:r2 e:l2e:l2 e:l3 e:l3e:r3 e:r4

G = (R, L, I) e:l1 e:r1 e:l1 e:r2• R = {r , r , ..., r } 1 2 n e:l2• L = {l , l , ..., l } 1 2 n e:l2 e:l3 e:l3• I = {i , i , ..., i } 1 2 n e:r3 e:r4

e:l1e:r1 e:l1 e:r2 e:l2e:l2 e:l3 e:l3e:r3 e:r4

e:l1 e:l1e:r1 e:l1 e:r2 e:r1 e:l1 e:r2 e:l2 e:l2e:l2 e:l3 e:l3 e:l2 e:l3 e:l3e:r3 e:r4 e:r3 e:r4

e:l1 e:l1e:r1 e:l1 e:r2 e:r1 e:l1 e:r2 e:l2 e:l2e:l2 e:l3 e:l3 e:l2 e:l3 e:l3e:r3 e:r4 e:r3 e:r4 e:l1e:r1 e:l1 e:r2 e:l2e:l2 e:l3 e:l3e:r3 e:r4

e:l1 e:l1e:r1 e:l1 e:r2 e:r1 e:l1 e:r2 e:l2 e:l2e:l2 e:l3 e:l3 e:l2 e:l3 e:l3e:r3 e:r4 e:r3 e:r4 e:l1 e:l1e:r1 e:l1 e:r2 e:r1 e:l1 e:r2 e:l2 e:l2e:l2 e:l3 e:l3 e:l2 e:l3 e:l3e:r3 e:r4 e:r3 e:r4

e:l1e:r1 e:l1 e:r2 e:l2e:l2 e:l3 e:l3e:r3 e:r4

The LDSD ontology Our own ontology, but could map with MuSim in the future

At a glance• A system providing recommendations for all DBpedia bands and artists (±40K) using LDSD • And explaining its recommendations • Both using Linked Data and Semantic Web standards (RDF, SPARQL)• Integrating related Web data for an improved user-experience

Architecture (2) Dataset reducing (1) Dataset (3) LDSD (4) Useridentiﬁcation computation interface RDF Data RDF Data

Dataset• Retrieving all artists and bands in DBpedia (±40K) • Including incoming / outcoming links • Approximately 3M triples• Removing datatype properties • 2.2M (75%)• Merging /ontology and /property • 1.7M (55%)

Distribution 20K+ artists (50%) are not linked to any other artist

Curation• 118 properties linking artists together • 18 mis-used, 35 wrongly defined (e.g. dbprop:klfsgProperty)• 578 properties linking artist to resources • 183 used only once, 36 wrongly defined• 767 properties linking resources to artists • 336 used only once, 115 wrongly defined• Dataset reduced to 1M triples

Computing distance• 9,797 minutes Done for all artists in DBpedia Artist Time (sec.) Ramones 25.20 • 2 x AMD Opteron 250 Johnny Cash 61.16 4GB Ubuntu 8.10 U2 50.06• 50M triples The Clash Bad Religion 43.34 34.98 • Modelled using the The Aggrolites 7.35 LDSD ontology Janis Joplin 23.12

Artist Distance Elvis Presley 0.0978June Carter Cash 0.1056 Willie Nelson 0.1322Kris Kristofferson 0.1407 Bob Dylan 0.1466 Marty Robbins 0.1673 Rosanne Cash 0.1782 Charlie McCoy 0.1836 Gene Autry 0.1910 Carl Smith 0.1980

User interface

Sorry, slideshare people,that’s a movie so youwon’t be able to see it !

Evaluation

Evaluation settings• Off-line and on-line user evaluation • Using common RecSys metrics• 10 subjects • 2 women, 8 men • 24 to 34 years old • 35 to 55 minutes per interview, F2F

Metrics• Off-line evaluation - comparison with last.fm • 5 artists / bands • 2 blind list, 10 ranked recommendations per list • Marks from 1 to 5• On-line recommendation - dbrec only • 5 artists / bands • Browsing recommendations using dbrec • Marks from 1 to 5, plus observations and interviews

dbrec vs last.fm• Average mark of recommendations • 3.37(±1.19) • 3.44(±1.25) w/ on-line • 3.69(±1.01) for last.fm

Results for the precision(t=X means items are Precisionrelevant if ranked X ormore)Cannot compute recall dbrec dbrec(implies users know allbands in the system) last.fm (off-line) (off+on-line) t=2 92.05 90.59 98.32 t=3 76.63 77.72 87.91 t=4 49.06 51.23 58.05 t=5 20.09 25 25.165

Novel recommendations• Lots of unknown recommendations • 62% for dbrec (59.6% w/ on-line) • 40.4% for last.fm • But that’s a good news !• Evaluated 274 of them on dbrec • 3.05(± 1.09)

Observations• Explanations for unknown bands • Checked for 198 / 310• But also for known ones • 24 / 190• Helped to understand the recommendation • Even if they already knew the band

Interviews User-interface Explanations Enjoyable 9 7 Useful 9 9 Enriching 8 10Easy to use 10 9 Confusing 0 2Complicated 0 2 Too geeky 1 6

Lessons learnt

Data quality• Issues with DBpedia properties • Misused : dbprop:notableInstruments • Wrongly deﬁned : dbprop:klfsgProperty • Duplicates : /ontology versus /property• Requires data curation ! • Automated and manual

Use, but replicate• More and more public SPARQL endpoints • Often limited to X max results • 5,000 on DBpedia But, that’s fair enough. Hosting a SPARQL endpoint is costly and• Difﬁcult to use in production opening-it up fully to anyone would require lots of maintenance, etc. • Requires local replica • But implies synchronisation !

Use, but replicateSELECT ?labelWHERE { ?x rdfs:label ?label . { ?x a dbpedia:MusicArtist } UNION { ?x a dbpedia:Band }}

Use, but replicate• Names of all DBpedia artists • Get number of results w/ COUNT • Run n/5000 queries (LIMIT + OFFSET) • Recompose results The query had more than 40K results, since most artists got their names• Network errors, etc. using different languages. So much more than 8 queries

SPARQL, Be quick or be neat • “List all artists / bands sharing common property-values with the current one” • Fits in a single SPARQL query • But does not scale • “Optimisation” has to be done manually by splitting the query and recomposing results using an external script

SPARQL, Be quick or be neat Tests done in the local RDF store 1: full-query 2: split by property 3: split by property- object Up to 75% faster Direct SPARQL Property-slicing Complete-slicing Queries Time Queries Time Queries Time Ramones 1 139.97 20 109.51 66 37.84 Johnny Cash 1 257.81 30 152.60 135 75.35 U2 1 155.53 22 122.91 70 44.03 The Clash 1 146.43 20 110.84 79 42.61 Bad Religion 1 104.08 23 86.49 97 47.35The Aggrolites 1 145.92 13 114.52 28 28.33 Janis Joplin 1 230.88 27 151.00 98 62.81

Next steps

Next steps• Other data sources • FreeBase, MusicBrainz, etc.• Distance improvement • Propagation, feature selection, etc.• User Interface • User-friendly explanations• LOD-compliance • Mapping with other ontologies, SPARQL endpoint

Conclusion• Deﬁned and applied a Semantic Distance measure to Linked Data• Used it to build a end-user music recommender system, with ±40K artists• Evaluated it using RecSys metrics• Learnt several domain-independent lessons regarding LOD consumption

Questions ?

Contact:alexandre.passant@deri.org - http://apassant.net - @terraces Acknowledgements: Science Foundation Ireland - SFI/08/CE/I1380 (Lion 2) References: AIII Spring Symposium 2010 - LinkedAI Symposium ESWC2010 - Demo Track ISWC2010 - In-Use Track

Pictures credits• http://flickr.com/photos/yumlog2/20896759/ by yuki*• http://richard.cyganiak.de/2007/10/lod/ by Richard Cyganiak and Anja Jentzsch• http://flickr.com/photos/loungerie/2196866243/ by loungerie• http://flickr.com/photos/iskanderstruck/248786430/ by iskanderbenamor• http://flickr.com/photos/homer4k/461407380/ by homer4k• http://flickr.com/photos/jpellgen/2390204986/ by jpellgen• http://flickr.com/photos/onegoodbumblebee/839927986/ by One Good Bumblebee• http://flickr.com/photos/28509009@N03/2668650475/ by marcreis• http://flickr.com/photos/8049973@N03/2656140464/ by wolf.tone

Dbrec - Music recommendations using DBpedia

by Alexandre Passant

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