visualization of music suggestions
DESCRIPTION
Final presentation for the thesis "visualization of music suggestions". Read the thesis text online at http://soundsuggest.wordpress.comTRANSCRIPT
Visualisatie van muziekaanbevelingen
Promotor:Prof. Dr. Ir. E. Duval, Prof. Dr. K. Verbert, Dr. J. KlerkxBegeleider:Prof. Dr. K. Verbert, Dr. J. Klerkx
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Een visueel uitlegsysteem voor collaboratieve filtering
Joris SCHELFAUT
Academiejaar 2012-2013
Recommender system
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• Compute personalized item suggestions based on the user’s interaction with the system– Listening history– Items ratings– Item purchases– …
• Last.fm, Netflix, IMDb, Facebook, Amazon, …
Recommender system
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• Database (items / users)
Recommender system
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• Database • Algorithms
Recommender system > CBF
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Recommender system > CF
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Black box problem
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Explanation system
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Explanation system > Examples
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Explanation system > evaluation
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Explanation system > evaluation
Objective
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• Make a visualization...that can explain music suggestions
• Interactive• Steer the process (if possible)• Evaluation based on previously described aims• Non-professional users (learnability)
Target audience Visualization design Implementation Evaluation results Conclusion Demo
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Target audience
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Visualization design
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Visualization design
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Visualization design
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Implementation > Recommender
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• Last.fm– Collaborative approach– Lots of data– Active users
• Last.fm API– Listening history– Neighbours– Recommendations
Implementation > Application
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• Chrome extension– Inject HTML into a webpage
Implementation > Visualization
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• D3.js– Lots of existing code– Well documented– Works in almost all
modern browsers
Evaluation > Iteration 1
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Evaluation > Iteration 1
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Evaluation > Iteration 1
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Evaluation > Iteration 1
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• Feasable?– Insight– Usability
• Think aloud / SUS• 5 Test users
Evaluation > Iteration 1
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• Feasable?– Yes– Rationale could be
discovered• SUS avg: 77
Evaluation > Iteration 2
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Evaluation > Iteration 2
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• Is the transformation from paper to digital successful?– Insight– Usability
• 5 test users• Think aloud / SUS
Evaluation > Iteration 2
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• Is the transformation from paper to digital successful?– Yes
• Issue: parallel edges are hard to distinguish• SUS avg: 79.5
Evaluation > Iteration 3
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Evaluation > Iteration 3
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• Real data– Insight: more relevant
data• Focus on usability– Option menu
• Insight• 5 test users• Think aloud / SUS
Evaluation > Iteration 3
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• Negative:– Threshold– Slow loading times– Distinguish between recommendations and
owned items– Learning
• SUS avg: 76.5
Evaluation > Iteration 4
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Evaluation > Iteration 4
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Evaluation > Iteration 4
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• Where changes positive?• Evaluating aims• 10 test users• Think aloud / SUS
Evaluation > Iteration 4
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• Positive:– Tension– Underlining owned items– Keeping current data in local storage
• Negative– Learning– Visual clutter when a showing approx. 40+ items
Evaluation > Iteration 4
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• SUS avg 80.5
Evaluation > Iteration 4
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• Transparency: yes.• Scrutability: no.• Trust: sometimes.• Effectiveness: sometimes.• Persuasiveness: sometimes.• Efficiency: yes.• Satisfaction: yes.
Conclusion > Objectives
• Varying levels of perceived usefulness• SUS score of 80.5 for iteration 4• Learnability can improve• Design can be effective for explaining
collaborative recommendations• Starting point for further exploration
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Conclusion > Future work
• Visualization– Use symmetry in data to retain users instead of artists as nodes– Additional interactions (e.g. edges)– Clutter reduction through opacity– Temporary hide users
• Data– Improve data load times through caching
• Learnability– Further improve labels and visual clues
• Evaluation– Benchmarks, expert-based, heuristic
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Demo
• https://chrome.google.com/webstore/detail/soundsuggest/jimmblcjmmjjfaklclmohcnabndlidmb?hl=nl&gl=BE
• http://www.last.fm/home/recs
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Stats
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Stats
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Stats
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Thank you!
• For your attention!• Special thanks to my supervisors Joris and
Katrien!
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Questions?
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