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One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio
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One Music, Many Listeners - A Case-based Song Scheduler for Group Customised Radio

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Poolcasting explained in a great slideshow - diagrams, sketches and screens. …

Poolcasting explained in a great slideshow - diagrams, sketches and screens.

By Claudio Baccigalupo and Enric Plaza

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  • 1. IIIA - CSIC A Case-based Song Scheduler for Group Customised Radio Claudio Baccigalupo – Enric Plaza Project Presentation – IIIA – February 2007
  • 2. ONE MUSIC, MANY LISTENERS music club radio juke-box home parties
  • 3. SOME COMMON PROBLEMS How to check the reactions of the audience? How to provide users with a large collection of music? How to prevent monopolistic selection of music? How to avoid annoying disruptions between songs ? How to match the tastes of all the listeners?
  • 4. OUR PROPOSED SOLUTION A novel group-based web-radio architecture Songs are broadcast from the libraries of the listeners Users can requests and evaluate songs for each channel The music is not pre-programmed, but selected in real-time by a CBR technique that combines both musical properties and listeners’ preferences to guarantee a fair satisfaction of the whole audience
  • 5. TODAY’S MENU 1. Introduction 2. The Poolcasting web-radio architecture 3. A Case-based Reasoning Song Scheduler The Participants’ Case Bases Musical Domain Knowledge The Retrieve Process The Reuse Process The Revise Process 4. Conclusions and future work
  • 6. WHAT IS POOLCASTING ?
  • 7. WHAT IS POOLCASTING ?
  • 8. WHAT IS POOLCASTING ?
  • 9. WHAT IS POOLCASTING ?
  • 10. WHAT IS POOLCASTING ?
  • 11. WHAT IS POOLCASTING ?
  • 12. WHAT IS POOLCASTING ?
  • 13. WHAT IS POOLCASTING ?
  • 14. WHAT IS POOLCASTING ?
  • 15. DESIRED PROPERTIES FOR A CHANNEL Variety: no song/artist should be repeated at a close distance Continuity: each song should be musically associated with the song it follows Listeners’ satisfaction: each song should match the musical preferences of (at least part of) the listeners Fairness: the more unsatisfied a listener, the more her preferences should influence the choice of the next song
  • 16. ARCHITECTURE OF A WEB-RADIO Database CHANNELS AVAILABLE SONGS LISTENERS collect information create streams to schedule a song for the channel Song Scheduler Stream Generator extract Songs list of uncompressed Web Interface scheduled listeners audio signal Buffer song Web create a Streaming Server scheduled page channel song request MP3 stream OGG stream channel Music Library (64 kbps) (256 kbps) information INTERNET Listener Listener Admin Visitor
  • 17. ARCHITECTURE OF POOLCASTING Database request to join PARTICIPANTS CHANNELS list of shared songs MUSIC POOL LISTENERS collect information create streams to schedule a song for the channel Song Scheduler Stream Generator Songs uncompressed list of Web Interface Web Interface audio signal listeners Buffer requests and Web create a Streaming Server feedback scheduled page channel song request joins download MP3 stream OGG stream channel channel song (64 kbps) (256 kbps) information INTERNET Participant Listener Listener Admin Visitor
  • 18. ARCHITECTURE OF POOLCASTING Database request to join PARTICIPANTS CHANNELS list of shared songs MUSIC POOL LISTENERS collect information create streams to schedule a song for the channel Song Scheduler Stream Generator Songs uncompressed list of Web Interface Web Interface audio signal listeners Buffer requests and Web create a Streaming Server feedback scheduled page channel song request joins download MP3 stream OGG stream channel channel song (64 kbps) (256 kbps) information INTERNET Participant Listener Listener Admin Visitor
  • 19. TODAY’S MENU 1. Introduction 2. The Poolcasting web-radio architecture 3. A Case-based Reasoning Song Scheduler The Participants’ Case Bases Musical Domain Knowledge The Retrieve Process The Reuse Process The Revise Process 4. Conclusions and future work
  • 20. THE CBR SCHEMA Listener requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback Variety Continuity Listeners’ satisfaction Fairness
  • 21. THE CBR APPROACH Problem: given the description of a channel H, the list of its current listeners, and the sequence of the recently scheduled songs, select which song to schedule next on channel H Retrieval: retrieve from the channel pool Φ(H) a subset of songs either recommended by some user or not recently played and musica!y associated with the last song scheduled Reuse: rank the retrieved set combining the preferences and the satisfaction degrees of the current listeners of H and schedule on channel H the best-ranked song Revise: update the listeners’ preferences and the musical associations according to the feedback of the audience
  • 22. TODAY’S MENU 1. Introduction 2. The Poolcasting web-radio architecture 3. A Case-based Reasoning Song Scheduler The Participants’ Case Bases Musical Domain Knowledge The Retrieve Process The Reuse Process The Revise Process 4. Conclusions and future work
  • 23. THE PARTICIPANTS’ CASE BASES Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 24. THE PARTICIPANTS’ CASE BASES Every Case Base contains the list of songs in the library of a participant and a preference degree for each of these songs The preference degree g(P, S) has values in [-1,1]; -1 means P hates song S, 1 means P loves S, and 0 reflects indifference We infer the values of g(P, S) from the listening experience data included in the music library of each participant We assume that the higher the rating assigned to a song and the higher the play count, the stronger the preference
  • 25. THE PREFERENCE DEGREE Si n(Si) m(Si) g(Si) 0 1 0.5 Take on me _ 1 0 0.5 Avg. rating: n = 3 Big in Japan _ Avg. play count: m = 3.5 -1 -1 -1 Venus The preference degree is a combination of the normalised rating and play count of each song: g ( P, S i ) = θ n ( P, S i ) + ( 1 - θ ) m ( P, S i ) θ= 0.5
  • 26. THE PREFERENCE DEGREE The preference degree g(P, S) can be evaluated also for songs not included in the library of a Participant We assume that if P does not own a song S but owns other songs quot;om the same artist of S, then the preference of P for S is her average preference for those songs S g(P,S) S g(P,S) 0.5 Take on me Forerever 0.5 songs by Alphaville Young 0.5 Big in Japan 0.5 In the Mood -1 Venus
  • 27. THE PARTICIPANTS’ CASE BASES Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 28. MUSICAL DOMAIN KNOWLEDGE Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 29. SONG AND ARTIST ASSOCIATIONS A human DJ knows from experience which songs are associated We use an automatic process to extract this knowledge from a large collection of playlists available on the Web We assume that the more the playlists where two songs/ artists co-occur and the closer the distance at which they occur, the higher their association Extracting such knowledge from playlists is much better for our goal than using a content-based method
  • 30. SONG ASSOCIATION DEGREE The song association degree from a song X to a song Y is a combination of the number of playlists where X and Y co- occur, the distances at which they occur, the order in which they occur and the popularity of the two songs f(X,Y) P(Y|X) = f(X) ∑q 1 f(X,Y) Let be the set = _ _ of playlists where f(X)·( f(Y)/f )β f(X)·( f(Y)/f )β X and Y occur:
  • 31. SONG ASSOCIATION DEGREE The song association degree from a song X to a song Y is a combination of the number of playlists where X and Y co- occur, the distances at which they occur, the order in which they occur and the popularity of the two songs f(X,Y) _ f(X)·( f(Y)/f )β { if |d(q,X,Y)| > δ 0 if |d(q,X,Y)| ≤ δ 1/|d(q,X,Y)| d(q,X,Y) > 0 w(q,X,Y) = α/|d(q,X,Y)| if |d(q,X,Y)| ≤ δ d(q,X,Y) < 0 ∑q w(q, X, Y) Let be the set s(X, Y) = _ α=0.75 of playlists where f(X)·( f(Y)/f )β β=0.5 X and Y occur: δ=3
  • 32. ARTIST ASSOCIATION DEGREE The artist association degree from an artist A to an artist B is a combination of the number of playlists where any song from A and B co-occur, the distances at which they occur and the popularity of the two artists: be the playlists where a song from A and a song from B occur: Let { if |d’(q,A,B)| > δ’ 0 w’(q,A,B) = 1/|d’(q,A,B)| if |d’(q,A,B)| ≤ δ’ ∑q w’(q, A, B) _ s’(A, B) = f’(A)·( f’(B)/f’ )β δ’=2
  • 33. SONG AND ARTIST ASSOCIATIONS We have mined a collection of about 600,000 playlists from MyStrands, with the parameters set to α = 0.75, β = 0.5, δ = 3 and δ’ = 2, discarding single occurrences and associations between songs from the same artist We have obtained association degrees for 112,238 distinct songs and for 25,881 distinct artists For example, the most associated artists for Abba are: Agnetha Faltskog, A-Teens, Chic, Gloria Gaynor, The 5th Dimension
  • 34. MUSICAL DOMAIN KNOWLEDGE Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 35. THE RETRIEVE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 36. THE RETRIEVE PROCESS First, we rate each song Z Φ(H) in the channel pool of H with a relevance value r(Y, Z) in [0,1] that expresses how much Z satisfies the conditions of variety and continuity Next, we retrieve the κ best rated songs along this function r(Y, Z) = 1 if some participant requested Z via the web-page r(Y, Z) = 0 if the same song/artist was recently played on H Otherwise, r(Y, Z) measures the musical association from Y (the last song scheduled on channel H) to Z, as follows:
  • 37. THE RETRIEVE PROCESS r(Y, Z) = s(Y, Z) + εu(Y, Z) + ε2v(Y, Z) + ε3s’(a(Y), a(Z)) s(Y, Z) → song association from Y to Z u(Y, Z) → association from songs of the artist of Y to Z v(Y, Z) → association from songs of artists associated with the artist of Y to Z s’(a(Y), a(Z)) → association from artist of Y to artist of Z [0,1] → controls the importance of these conditions ε ε=0.5
  • 38. THE RETRIEVE PROCESS r(Y, Z) = s(Y, Z) + εu(Y, Z) + ε2v(Y, Z) + ε3s’(a(Y), a(Z)) Example: Y = Waterloo (Abba) s(Y, Z) : Z Waterloo u(Y, Z) : Mamma mia S.O.S. Fernando Z … (A-Teens) (Agnetha (Gloria v(Y, Z) : Z Faltskog) Gaynor) (Chic) s’(a(Y), a(Z)) : a(Z) (Abba)
  • 39. THE RETRIEVE PROCESS u(Y, Z) is the average song association degree from every song whose artist is a(Y) to Z: ∑W (Y,Z) s(W,Z) u(Y,Z) = #( (Y,Z)) (Y,Z) = {W | s(W,Z) > 0 a(Y) = a(W)} where v(Y, Z) is the average song association degree from every song whose artist is associated with a(Y) to Z, combined with the relative artist association degree: ∑W (Y,Z) s(W,Z) s’(a(W),a(Z)) v(Y,Z) = #( (Y,Z)) where (Y,Z) = {W | s(W,Z) > 0 s’(a(Y),a(W)) > 0}
  • 40. THE RETRIEVE PROCESS Example of retrieve process for the ‘80s Music Channel, where Y = Super Trouper (Abba) and Φ(H)= {Z1, Z2, Z3, Z4, Z5} Zi f(Zi) s(Y,Zi) u(Y,Zi) v(Y,Zi) s’(a(Y),a(Zi)) r(Y,Zi) Mamma Mia --- --- --- --- --- 0 5º (Abba) 1341 0.942 0.574 0.324 2.817 1.662 Take on me 2º (1937) 103 103 103 103 103 (A-Ha) 184 0.841 3.281 2.548 1.119 0.265 Listen to your heart 1º (642) 103 103 103 103 103 (Roxette) 237 0.944 1.234 1.807 0.852 The look of love 3º 0 (878) 103 103 103 103 (ABC) 278 0.114 0.428 0.614 1.063 I’m So Excited 0 4º (The Pointer Sisters) (1149) 103 103 103 103 19533 0.153 0.858 0.235 0.320 Mr. Brightside 0 (45787) 103 103 103 103 (The Ki!ers)
  • 41. THE RETRIEVE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 42. THE REUSE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 43. THE REUSE PROCESS This process ranks the retrieved set according to the preferences of the current listeners of the channel and their group satisfaction, and returns the best ranked song as the next song to be scheduled on the channel The most critical challenge is how to combine different individual preferences into one group satisfaction value To guarantee fairness among listeners, we propose a weighted average of the individual preferences, where the weight associated to each listener depends on her satisfaction about the last scheduled songs
  • 44. AVERAGE WITHOUT MISERY Let (H,t) be the listeners of channel H at time t and (H,t) the retrieved set; we define the group preference of (H,t) for a retrieved song S (H,t) as G(S,H,t) [-1,1] by cases: (average) if none of the current listeners hates song S, then we use a weighted average of the individual preferences for S: ∑P (H,t) g(P,S) [1-ω(P,H,t)] G(S,H,t) = #( (H,t)) (H,t): g(P, S) < μ, then P (without misery) otherwise, if we set the group preference for S to the lowest possible value: G(S,H,t) = -1 μ = -0.75
  • 45. AVERAGE WITHOUT MISERY This strategy is Pareto-optimal: if at least one listener prefers a song S to a song S’ and nobody prefers S’ to S, then G(S,H,t) ≥ G(S’H,t) This strategy avoids misery: if at least one listener has a bad preference for S’ (lower than a threshold μ), and no listener has a bad preference for S (lower than the threshold μ), then G(S,H,t) ≥ G(S’H,t) Using this strategy, we endeavour to schedule songs for which every current listener has an individual preference at least equal to μ
  • 46. AVERAGE WITHOUT MISERY ∑P (H,t) g(P,S) [1-ω(P,H,t)] G(S,H,t) = #( (H,t)) The weight [1-ω(P,H,t)] biases the weighted average in favour of the listeners more unsatisfied with the songs recently scheduled on the channel H ω(P, H, t) is the channel satisfaction degree of a listener P at a time t with respect to the music scheduled on H To evaluate ω(P, H, t) we combine the satisfaction degrees of P for each song scheduled on H since P is listening to H
  • 47. SONG SATISFACTION Let (P,H,t) = (X1,X2,…,Xz) be the set of songs scheduled on H since P began listening to H. We define the song satisfaction (P,H,t) scheduled at time ^ < t as: degree of P for a song Xi t e(P, Xi, H) = g(P, Xi) - maxS g(P, S) + 1 ^ (H,t) e(P, Xi, H) takes values [-1,1] and equals 1 only when the scheduled song Xi was the most preferred song by P in the ^ retrieved set (H,t) at time ^ t Combining the song satisfaction degrees of P for the songs in (P,H,t) we can estimate the channel satisfaction degree ω(P, H, t)
  • 48. CHANNEL SATISFACTION Since satisfaction is an emotion that wears off with time, we combine the song satisfaction degrees assigning more importance the the most recent songs We use the following geomtric series: ∑ iz= 1 χz-i e(P, Xi, H) where χ [0,1] is the decay rate of satisfaction over time We normalise this series to return values in the interval [0,1] and define the channel satisfaction degree for P as: ( ) 1 ∑ iz= 1 χz-i e(P, Xi, H) + 1 ω(P, H, t) = 2 χ = 0.8
  • 49. THE REUSE PROCESS / AUTOMATIC Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 50. THE REUSE PROCESS / INTERACTIVE Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 51. THE REUSE PROCESS / INTERACTIVE
  • 52. THE REUSE PROCESS / INTERACTIVE If a listener P sends an explicit preference towards a scheduled song Z via the Poolcasting web-page, then the implicit knowledge g(P, Z) that was stored in the Case Base of P (inferred from the music library listening experience) is replaced with this new explicit evaluation provided Next, since the Case Base has changed, the retrieved set is re- ranked, and the new most group-preferred song is scheduled This process continues until the previous song scheduled is played and Z is downloaded to the local buffer; then the CBR process restarts to schedule the next song
  • 53. THE REUSE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 54. THE REVISE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 55. THE REVISE PROCESS
  • 56. THE REVISE PROCESS S g(P,S) S g(P,S) La Isla Bonita La Isla Bonita 0.5 -1 (Madonna) (Madonna) I’m Your Man (Wham!) I’m Your Man (Wham!) s(Y,Z) = 0.5 s(Y,Z) = 0 La Isla Bonita (Madonna) La Isla Bonita (Madonna)
  • 57. THE REVISE PROCESS Song Associations Listener local to the channel requests song candidate best ranked REUSE RETRIEVAL songs song INPUT Song and Artist (channel description, Associations inferred last songs scheduled, Listeners' from public playlists current listeners) satisfaction Listener evaluates CASE BASES scheduled song PREFERENCE SONGS Analysis REVISE MODEL PREFERENCE SONGS Analysis MODEL PREFERENCE SONGS Analysis MODEL Listener sends feedback
  • 58. TODAY’S MENU 1. Introduction 2. The Poolcasting web-radio architecture 3. A Case-based Reasoning Song Scheduler The Participants’ Case Bases Musical Domain Knowledge The Retrieve Process The Reuse Process The Revise Process 4. Conclusions and future work
  • 59. CONCLUSIONS Poolcasting proposes a new paradigm for web-radios, shifting from a classic monolithic approach where “One controls, many listen”, to a new decentralised approach where “Many control, many listen” A Poolcasting web-radio will be up and running in our Intranet in the next months, ready for tests and evaluations Users need iTunes and a web sharing service to participate We assume users will enjoy listening to music from other libraries, for they can easily get to discover new music Tests should include both passive and active listeners
  • 60. CONTRIBUTION TO CBR The Song Scheduler works with multiple participants’ case bases and with domain knowledge acquired from playlists containing listening experiences of a large number of users The collection of case bases is open and dynamic The Reuse Process combines data and preferences from different case bases (modeling users’ listening experiences), and generates a globally good sequence of solutions over a period of time – not just one “group solution” for a problem Both intensive knowledge and preference models are used in the Retrieve and Reuse processes, while users’ feedback is used in the Revise process to improve customisation
  • 61. FUTURE WORK Testing the CBR Song Scheduler with different parameters Dealing with the issues of copyright and privacy Introducing a reputation degree for the listeners Extending the users’ preference models with other listening experience data (e.g., personal playlists, song recency) Investigate how to apply this approach to other musical contexts where a group of persons gathers to listen to the same stream of music
  • 62. ANY QUESTION ? IIIA - CSIC Here are some recommended papers: C. Baccigalupo, E. Plaza (2006) Case-based Sequential Ordering of Songs for Playlist Recommendation J. Masthoff (2004) Group modeling: Selecting a sequence of television items to suit a group of viewers K. McCarthy, L. McGinty, B. Smyth and M. Salamó (2006) The Needs of the Many: A Case-Based Group Recommender System

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