Distributed, Real-Time Computation of Community Preferences Thomas Lutkenhouse, Michael L. Nelson, Johan Bollen Old Dominion University Computer Science Department Norfolk, VA 23529 USA {lutken,mln,jbollen}@cs.odu.edu HT 2005 - Sixteenth ACM Conference on Hypertext and Hypermedia 6.-9.Sept. 2005, Salzburg Austria
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Distributed, Real-Time Computation of Community Preferences Thomas Lutkenhouse, Michael L. Nelson, Johan Bollen Old Dominion University Computer Science.
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Distributed, Real-Time Computation of Community Preferences
Thomas Lutkenhouse, Michael L. Nelson, Johan Bollen
Old Dominion UniversityComputer Science Department
Norfolk, VA 23529 USA
{lutken,mln,jbollen}@cs.odu.edu
HT 2005 - Sixteenth ACM Conference on Hypertext and Hypermedia
6.-9.Sept. 2005, Salzburg Austria
Distributed, Real-Time
Computation of
Community Preferences
not CS if you don’t compute
changes are immediate
no central state
not personalization
Outline
• Review of technologies– buckets– Hebbian learning– previous results
Experiment• Spin Magazine’s “Top 50 Rock Bands of All Time”
– something other than reports, journals, etc.– harvest allmusic.com for metadata for all LPs by the 50 bands
(total = 800 LPs)
• Maintain hierarchical arrangement– 1 artist N albums
• Initialize the network of 800 LPs with each LP randomly linked to 5 other LPs
• Send out email invitations to browse the network– have them explore, and then examine the resulting network– users not informed about the workings of the network
• Compare the results to:– Other “expert” lists (VH1, DigitalDreamDoor,
original Spin Magazine list)– Artist / LP best seller according to RIAA– Artist / LP Amazon sales rank
Expert Rankings
• No correlation with:– VH1 artist list– DigitalDreamDoor list– original Spin Magazine list (!)
(critics don’t agree with each other, or the record buying public)
RIAA Results
• RIAA had only– only 51/800 LPs– only 14/50 artists
(critics don’t buy records!)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
All Bands Top 50% Top 20% Top 10%
Rank
Probability of being a bestseller
Degree Centrality
Weighted Degree Centrality
Page Rank
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
All albums Top 50% Top 20% Top 10% Top 5% Top 2% Top 1%
Rank
Probability of being a best seller
Degree Centrality
Weighted Degree Centrality
Page Rank
Figure 6. Probability of albums being best-sellers.
Figure 7. Probability of artists being best-sellers.
*RIAA sales caveat
Amazon Sales Rank
• No correlation with individual LP sales rank…
• …but correlated with mean artist sales rank– similar to RIAA data– interpretation: popular artists often have
obscure LPs
Relatedness(?)
Relatedness(?)
Lessons Learned
• While the subject matter was interesting, it was oriented for music geeks
• i.e., no actual music was delivered to the users (intellectual property considerations)
• more traversals needed
• Random initial starting points were difficult to overcome
• “cold start problem” - pre-seed the links according to some criteria?• weights did not decay over time/traversals
• Choosing only artists from Spin Magazine may have pre-filtered the response
• choose artists from Down Beat (Jazz), Vibe (Urban), Music City News (Country), etc.
Conclusions
• Can build a network of smart objects featuring adaptive, hierarchical links constructed in real-time without central state– network is created without latency and with
computations amortized over individual accesses
• Experimental testbed with popular music LP metadata shown to approach sales rank of artists, not LPs