Scope of the researchDevelop an intelligent techniqueto satisfy a group of listenersby delivering a sequence of songsadapted for the entire audience
Desired propertiesVariety avoiding repetitionsSmoothness nice musical transitionsCustomisation adapted for the audienceFairness satisfactory for everyone
Structure of the thesis1. Introduction2. Musical associations smoothness3. Individual listening behaviours customisation4. The poolcasting CBR technique fairness5. Poolcasting Web radio6. Experiments and evaluation7. Conclusions
Chapter 2.Musical associations froma Web of experiences
State of the artMethods to uncover associated songs:experts-based not scalablecontent-based ignore cultural liaisonssocial-based observing how people use music in their activities
Playlists Co-occurrence analysis X YHow often do X and Y occur in the same playlists? Dothey always occur in the same order? Contiguously?
Playlists Co-occurrence analysis X s(X, Y ) Ys(X, Y ) ∈ [0, 1] measures the association between Xand Y based on their co-occurrences in a set of playlists
From playlists to associations Initial data set: 993,825 playlists Fig 2.2 50,000 300,000 songs 40,000 ! ! artistsNumber of playlists Number of playlists 200,000 30,000 20,000 100,000 10,000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 4 8 12 16 20 24 28 32 36 40 Alphabetically ordered songs/artists [limited to 1~15] Number of songs [limited to 1~40] After noise removal: 465,438 playlists s(X, Y ) estimated for ~400K songs by ~50K artists
Lists of associated songsTop associated songs with ‘New York, New York’:1. ‘The Waters of March’ (Susannah McCorkle)2. ‘Stardust’ (Glenn Miller)Top associated artists with Frank Sinatra:1. Dean Martin the same result of2. Sammy Davis Jr.
Structure of the thesis1. Introduction2. Musical associations s(X, Y )3. Individual listening behaviours customisation4. The poolcasting CBR technique fairness5. Poolcasting Web radio6. Experiments and evaluation7. Conclusions
From habits to implicit preferences Implicit user modeling U i(U, X) Xi(U, X) ∈ [0, 1] estimates the implicit preference of U fora song X combining the observed rating and play count
Structure of the thesis1. Introduction2. Musical associations s(X, Y )3. Individual listening behaviours i(U, X)4. The poolcasting CBR technique fairness5. Poolcasting Web radio6. Experiments and evaluation7. Conclusions
Overview U1 U2 U3 C1 C2 C3Poolcasting Poolcasting Poolcasting H1 H2 H3 ... T =1 T =2 T =3 time
Adding one song to the sequence U3 C3 Poolcasting Case-Based Reasoning H1 H2 T =1 T =2 T =3 time
A collection of Case BasesBuild one Case Base for each user U3 C3 X i(U1 , X) Listening habits 0.5 −0.7 Case Bases U1 ... X i(U2 , X) Individual preferences 1.0 0.2 U2 ... H1 H2 T =1 T =2 T =3 time
The Retrieve processExtract from the Case Bases a U3 C3subset of songs Y that: Listening habits- have not been played recently Case Bases variety Retrieve- maximise the degree s(H2 , Y ) Individual preferences smoothness Musical association H1 H2 T =1 T =2 T =3 time
The Reuse processRank the retrieved set according U3 C3to the aggregated preferences of Listeningall the members of the audience habits Case Bases customisation Retrieve fairness Individual preferences Musical association Reuse H1 H2 H3 T =1 T =2 T =3 time
The Revise processUpdate the implicit preferences U3 C3with the users’ explicit feedback Listening habitsimplicit Case Basesi(U, X) Revise Retrieve preference p(U, X, T ) Individual preferencesexplicite(U, X, T ) Musical association Reuse H1 H2 H3 ... T =1 T =2 T =3 time
The iterated CBR technique U1 U2 U3 C1 C2 C3 Listening Listening Listening habits habits habits Case Bases Case Bases Case Bases Revise Revise ReviseRetrieve Retrieve Retrieve Individual Individual Individual preferences preferences preferences Musical Musical association association Reuse Reuse Reuse H1 H2 H3 ... T =1 T =2 T =3 time
Aggregating individual preferencesFrom multiple preference degrees p(U, X, T ) ∈ [−1, 1] :X p(U1 , X, T ) p(U2 , X, T ) p(U3 , X, T )to an aggregated group-preference g(X, T ) ∈ [−1, 1] :
Aggregating individual preferencesFrom multiple preference degrees p(U, X, T ) ∈ [−1, 1] :X p(U1 , X, T ) p(U2 , X, T ) p(U3 , X, T )to an aggregated group-preference g(X, T ) ∈ [−1, 1] : p(U, X, T )X g(X, T ) = (1 − q(U, T − 1)) · #(UT ) U ∈UT weight averagedeﬁned as a satisfaction-weighted average
Avoiding miseryThe satisfaction-weighted aggregation g(X, T ) ∈ [−1, 1]is completed with a measure intended to avoid misery:assign the minimum degree if any user strongly dislikes X −1 if ∃U ∈ UT : p(U, X, T ) < µg(X, T ) = p(U, X, T ) (1 − q(U, T − 1)) · otherwise. U ∈UT #(UT )This results is an acceptable compromise for the group
Structure of the thesis1. Introduction2. Musical associations3. Individual listening behaviours4. The poolcasting CBR technique5. Poolcasting Web radio6. Experiments and evaluation7. Conclusions
The Poolcasting radio architectureplaylists Database MUSIC POOL MUSICAL ASSOCIATIONS CURRENT LISTENERSmetadata PREFERENCES CHANNELS list of list of available listeners shared songs songs knowledge to Stream Generator ratings and schedule play counts audio signal Library Parser Song Scheduler Streaming Server upload song OGG stream (256 Kbps) share library rate songs create channel MP3 stream Web Interface (64 Kbps) I N T E R N E T Media Personal Library Participant Player Participant
Subjective evaluationPoolcasting Web radio as a test platform for one year10 users sharing 24,763 identiﬁed songs4,828 preferences inferred from personal librariesPositive feedback for the overall experienceVariety requirement was too weakSmootness requirement was too strong
Contributions Musical Tasks Playlists Associations Musical Experience Poolcasting Sequence Web Group Individual Customisation Listening habits Preferences1. Reinterpretation of Case-Based Reasoning2. Mining the Web for valuable experiential data3. Iterated social choice and preference aggregation4. A social Web radio application
Future work Content Delivers a sequence of items … Poolcasting system to satisfy the group of people Audience1. Generalising poolcasting to other domains2. Abstracting the iterated social choice problem3. Uncovering associations for movies, TV shows, …
Publications[ECCBR ’06] Baccigalupo and Plaza. Case-based sequential ordering of songs forplaylist recommendation. In Proceedings of the 8th European Conference on Case-Based Reasoning, volume 4106 of Lecture Notes in Computer Science, pages 286–300, Springer 2006.[ICCBR ’07] Baccigalupo and Plaza. A case-based song scheduler for groupcustomised radio. In Proceedings of the 7th International Conference on Case-Based Reasoning, volume 4626 of Lecture Notes in Computer Science, pages 433–448, Springer 2007. Best Application Paper[ECML ‘07] Baccigalupo and Plaza. Mining music social networks for automatingsocial music services. In Workshop Notes of the ECML/PKDD 2007 Workshop onWeb Mining 2.0, pages 123–134, 2007.
Publications[AXMEDIS ‘07] Baccigalupo and Plaza. Poolcasting: a social Web radio architecturefor group customisation. In Proceedings of the 3rd International Conference onAutomated Production of Cross Media Content for Multi-Channel Distribution,pages 115–112, IEEE Computer Society 2007.[ICMC ‘07] Baccigalupo and Plaza. Sharing and combining listening experience: asocial approach to Web radio. In Proceedings of the 2007 International ComputerMusic Conference, pages 228–231, 2007.[ISMIR ‘08] Baccigalupo, Plaza, and Donaldson. Uncovering afﬁnity of artists tomultiple genres from social behaviour data. In Proceedings of the 8th InternationalConference of Music Information Retrieval (ISMIR), pages 275–280, 2008.[ICCBR ‘09] Plaza and Baccigalupo. Principle and praxis in the experience Web: acase study in social music. In Proceedings of the ICCBR 2009 Workshops, pages 55–63, University of Washington Tacoma, 2009.