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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