EC-Web 2012 - 13th International Conference on Electronic Commerce and Web Technologies                        Vienna (Aus...
Some stats              28,000,000 songs available on iTunes Store (*)                      around 31,000 hours of music a...
exponential growth                of the available musicC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveragi...
online music libraries totally   changed our listening habitsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Lev...
Information OverloadC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate ...
paradox of choice                           (Barry Schwartz, TED talk “Why more is less”)C. Musto, G. Semeraro, P. Lops, M...
what music should I listen to?C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to...
solution                       personalization.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social...
solution personalized music playlistsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sou...
Is this something new?                                                                                      No.C. Musto, G...
Amazon.com                                                            RecommendationsC. Musto, G. Semeraro, P. Lops, M. de...
Genius @iTunes                                               RecommendationsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, ...
All the state of the art             platforms share an            important drawback.C. Musto, G. Semeraro, P. Lops, M. d...
need for                                                                 explicit                                         ...
training is a bottleneck.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Gene...
social media    changed the rules for informationmanagement and knowledge acquisitionC. Musto, G. Semeraro, P. Lops, M. de...
social media   give new opportunities for modeling andgathering information about user preferencesC. Musto, G. Semeraro, P...
Facebookuser preferences in music.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Source...
Our contribution                                        Play.meC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.L...
Play.me                               personalized music playlists      • Goal        • To provide users with personalized...
Play.me                                             architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
Play.me                                             architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
Myusic                                          pre-processing   • Crawling from Last.fm    • Public API    • Content-base...
Myusic                                          pre-processing                     Sigur Ròs tag cloud from Last.fmC. Must...
Play.me                                             architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
Myusic                           data extraction from FacebookC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Le...
Myusic                           data extraction from Facebook         explicit preferencesC. Musto, G. Semeraro, P. Lops,...
Myusic                           data extraction from Facebook        implicit preferencesC. Musto, G. Semeraro, P. Lops, ...
Play.me                                             architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
Play.fm                                             enrichment   • Rationale    • Given a set of explicit preferences extr...
Play.fm                                     enrichment example                                                         Col...
Play.me                                             architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
Play.me                                                  playlistMost popular songs of the artists extracted from Last.fm ...
let’s go                                                                               deeperC. Musto, G. Semeraro, P. Lop...
Play.fm                                             enrichment   • Comparison of two approaches       •       Content-base...
Play.fm                enrichment based on Distributional Models       • Content-based strategy       • Each artist is rep...
distributional models insightby analyzing large corpus of textual data it is possibleto infer information about the usage ...
distributional models                                                           “meaning                                  ...
distributional models insightby analyzing large corpus of textual data it is possibleto infer information about the usage ...
distributional models                               term/context matrix (WordSpace)                               c1      ...
distributional models                                       beer vs. glass: good overlap                               c1 ...
distributional models                                         beer vs. spoon: no overlap                               c1 ...
distributional models                          rock vs. post rock = good overlap                                          ...
distributional models                              rock vs. classical = no overlap                                        ...
WordSpace                                                 example                                           rock          ...
representation of documents (*)   can be inferred by combining the representation of   the terms (**) occurring in the doc...
distributional models                         term/context matrix (DocSpace)                               c1       c2    ...
Play.fm                enrichment based on Distributional Models                                         Coldplay         ...
Play.fm                enrichment based on Distributional Models                     input: vector space representation   ...
Linked Open Data CloudC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generat...
Linked Open Data Cloud                                                                   Structured                       ...
Play.fm                        enrichment based on Linked Data                      Coldplay play Alternative RockC. Musto...
Play.me                                              RDF triple              Relationships are explictly encoded in RDF.C....
Play.fm                        enrichment based on Linked Data       • Linked Open Data Cloud       • Each artist is mappe...
Play.fm                        enrichment based on Linked Data                                  input: SPARQL query       ...
recap                                      enrichment process   input: artist                                             ...
experimental       evaluation.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to...
experimental design  • Experiment   • Which one is the enrichment technique that           can provide users with the best...
experimental design                                                 settings     • 30 users         • Heterogeneous musica...
experimental setup            Given a playlist, each user can freely express her own               feedback (like/dislike)...
experimental setup   Experiment repeated three times (one run with Linked Data enrichment,    another one with Distributio...
experimental setup                     Users were unaware of the adopted configuration.C. Musto, G. Semeraro, P. Lops, M. d...
experimental design                                                  results                   76.3     80                ...
experimental design                                                  results                   76.3     80                ...
experimental design                                                  results                   76.3     80                ...
experimental design                                                  results                   76.3     80                ...
Play.me                                 recap of the experiment    • Play.me     • Platform for generating personalized mu...
conclusions.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personal...
both enrichment techniques                 overcome the baselineC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci....
distributional models                      overcome linked dataC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.L...
future research.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Pers...
merging different                      enrichment techniquesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leve...
evaluation with user-based metrics                          (serendipity, novelty, unexpectedness)C. Musto, G. Semeraro, P...
modeling context.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Per...
questions?C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personaliz...
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  • Leveraging Social Media Sources to Generate Personalized Music Playlists

    1. 1. EC-Web 2012 - 13th International Conference on Electronic Commerce and Web Technologies Vienna (Austria), 04.09.2012 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis, Fedelucio Narducci Leveraging Social Media Sources to Generate Personalized Music Playlists
    2. 2. Some stats 28,000,000 songs available on iTunes Store (*) around 31,000 hours of music a typical user spends 1.5 hours for day listening to music = 56 years to listen to the whole iTunes Library (*) http://www.digitalmusicnews.com/permalink/2012/120425itunesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    3. 3. exponential growth of the available musicC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    4. 4. online music libraries totally changed our listening habitsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    5. 5. Information OverloadC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    6. 6. paradox of choice (Barry Schwartz, TED talk “Why more is less”)C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    7. 7. what music should I listen to?C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    8. 8. solution personalization.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    9. 9. solution personalized music playlistsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    10. 10. Is this something new? No.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    11. 11. Amazon.com RecommendationsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    12. 12. Genius @iTunes RecommendationsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    13. 13. All the state of the art platforms share an important drawback.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    14. 14. need for explicit information about user interests.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    15. 15. training is a bottleneck.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    16. 16. social media changed the rules for informationmanagement and knowledge acquisitionC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    17. 17. social media give new opportunities for modeling andgathering information about user preferencesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    18. 18. Facebookuser preferences in music.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    19. 19. Our contribution Play.meC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    20. 20. Play.me personalized music playlists • Goal • To provide users with personalized music playlists • Insights • Extraction of explicit user preferences from Facebook • Playlist creation by enriching explicit user preferences. • New artists are added to those explicitly extracted from Facebook • Comparison of two enrichment techniquesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    21. 21. Play.me architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    22. 22. Play.me architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    23. 23. Myusic pre-processing • Crawling from Last.fm • Public API • Content-based features • Name of the artist + Social tags • Noise processing • Information locally storedC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    24. 24. Myusic pre-processing Sigur Ròs tag cloud from Last.fmC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    25. 25. Play.me architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    26. 26. Myusic data extraction from FacebookC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    27. 27. Myusic data extraction from Facebook explicit preferencesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    28. 28. Myusic data extraction from Facebook implicit preferencesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    29. 29. Play.me architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    30. 30. Play.fm enrichment • Rationale • Given a set of explicit preferences extracted from Facebook • Play.me enrichs this set • Extraction of artists similar to those the user explicity likesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    31. 31. Play.fm enrichment example Coldplay extracted from Facebook enrichment radiohead red hot chili peppers kings of leonC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    32. 32. Play.me architectureC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    33. 33. Play.me playlistMost popular songs of the artists extracted from Last.fm (as well as those added through the enrichment) are proposed to the user.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    34. 34. let’s go deeperC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    35. 35. Play.fm enrichment • Comparison of two approaches • Content-based strategy • Distributional Models • Linked DataC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    36. 36. Play.fm enrichment based on Distributional Models • Content-based strategy • Each artist is represented through a set of tags • Each artist is represented as a point in a semantic geometrical space • Distributional Models • Similarity calculations to extract the most similar artists.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    37. 37. distributional models insightby analyzing large corpus of textual data it is possibleto infer information about the usage (about the meaning)of the terms.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    38. 38. distributional models “meaning is its use” L.Wittgenstein (Austrian philosopher)C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    39. 39. distributional models insightby analyzing large corpus of textual data it is possibleto infer information about the usage (about the meaning)of the terms. exampleC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    40. 40. distributional models term/context matrix (WordSpace) c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
    41. 41. distributional models beer vs. glass: good overlap c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
    42. 42. distributional models beer vs. spoon: no overlap c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
    43. 43. distributional models rock vs. post rock = good overlap a1 a2 a3 a4 a5 a6 rock ✔ ✔ ✔ post rock ✔ ✔ jazz ✔ classical ✔ ✔ ✔C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    44. 44. distributional models rock vs. classical = no overlap a1 a2 a3 a4 a5 a6 rock ✔ ✔ ✔ post rock ✔ ✔ jazz ✔ classical ✔ ✔ ✔C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    45. 45. WordSpace example rock post-rock jazz classicalC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    46. 46. representation of documents (*) can be inferred by combining the representation of the terms (**) occurring in the document. (*) documents = artists (**) terms = tagsC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    47. 47. distributional models term/context matrix (DocSpace) c1 c2 c3 c4 c5 c6 c7 c8 c9 t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ d1 ✔ ✔ ✔ ✔ ✔C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    48. 48. Play.fm enrichment based on Distributional Models Coldplay Radiohead Kings of Leon Lady GagaC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    49. 49. Play.fm enrichment based on Distributional Models input: vector space representation output: artists with the highest cosine similarity radiohead the killers kings of leonC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    50. 50. Linked Open Data CloudC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    51. 51. Linked Open Data Cloud Structured (RDF) representation of the information stored in Wikipedia.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    52. 52. Play.fm enrichment based on Linked Data Coldplay play Alternative RockC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    53. 53. Play.me RDF triple Relationships are explictly encoded in RDF.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    54. 54. Play.fm enrichment based on Linked Data • Linked Open Data Cloud • Each artist is mapped on a DBpedia node. • univocal URI • Relationship between artists (nodes) are explicitly encoded • e.g. genre, artist category, etc. • Use of SPARQL to extract artists (nodes) that share the same featuresC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    55. 55. Play.fm enrichment based on Linked Data input: SPARQL query output: artists sharing the same properties radiohead the smiths the verveC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    56. 56. recap enrichment process input: artist output: similar artists coldplay the smiths Linked Data radiohead the verve kings of leon Distributional Models radioheadC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    57. 57. experimental evaluation.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    58. 58. experimental design • Experiment • Which one is the enrichment technique that can provide users with the best playlists ?C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    59. 59. experimental design settings • 30 users • Heterogeneous musical knowledge • Last.fm crawl: 228,878 artists • Extraction & Recommendation step • 325 artists extracted • 11 per user, on averageC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    60. 60. experimental setup Given a playlist, each user can freely express her own feedback (like/dislike) on the proposed tracks.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    61. 61. experimental setup Experiment repeated three times (one run with Linked Data enrichment, another one with Distributional Models, one with a simple baseline based on popularity).C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    62. 62. experimental setup Users were unaware of the adopted configuration.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    63. 63. experimental design results 76.3 80 75.2 Linked Data Distributional Models Baseline (Popularity) 73.75 69.7 67.5 65.9 64.6 61.25 63.2 58 58 58 55 n=1 n=2 n=3 n = number of artists added for each extracted artistC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    64. 64. experimental design results 76.3 80 75.2 Linked Data Distributional Models Baseline (Popularity) 73.75 69.7 67.5 65.9 64.6 61.25 63.2 58 58 58 55 n=1 n=2 n=3 distributional models overcome linked dataC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    65. 65. experimental design results 76.3 80 75.2 Linked Data Distributional Models Baseline (Popularity) 73.75 69.7 67.5 65.9 64.6 61.25 63.2 58 58 58 55 n=1 n=2 n=3precision in distributional models drops down more rapidlyC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    66. 66. experimental design results 76.3 80 75.2 Linked Data Distributional Models Baseline (Popularity) 73.75 69.7 67.5 65.9 64.6 61.25 63.2 58 58 58 55 n=1 n=2 n=3good results for baseline, as well (poor music knowledge?)C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    67. 67. Play.me recap of the experiment • Play.me • Platform for generating personalized music playlists •Extraction of favourite artists from Facebook • Experiments • Enrichment based on distributional models overcomes linked data-based one in a significant wayC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    68. 68. conclusions.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    69. 69. both enrichment techniques overcome the baselineC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    70. 70. distributional models overcome linked dataC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    71. 71. future research.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    72. 72. merging different enrichment techniquesC. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    73. 73. evaluation with user-based metrics (serendipity, novelty, unexpectedness)C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    74. 74. modeling context.C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
    75. 75. questions?C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci.Leveraging Social Media Sources to Generate Personalized Music Playlists - EC-WEB 2012 - 04.09.12
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