Visualization of Music Suggestions

Visualisatie van muziekaanbevelingen

Promotor:Prof. Dr. Ir. E. Duval, Prof. Dr. K. Verbert, Dr. J. KlerkxBegeleider:Prof. Dr. K. Verbert, Dr. J. Klerkx

FACULTEIT WETENSCHAPPEN

Een visueel uitlegsysteem voor collaboratieve filtering

Joris SCHELFAUT

Academiejaar 2012-2013

Recommender system

FACULTEIT WETENSCHAPPEN

• 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

FACULTEIT WETENSCHAPPEN

• Database (items / users)

Recommender system

FACULTEIT WETENSCHAPPEN

• Database • Algorithms

Recommender system > CBF

FACULTEIT WETENSCHAPPEN

Recommender system > CF

FACULTEIT WETENSCHAPPEN

Black box problem

FACULTEIT WETENSCHAPPEN

Explanation system

FACULTEIT WETENSCHAPPEN

Explanation system > Examples

FACULTEIT WETENSCHAPPEN

Explanation system > evaluation

FACULTEIT WETENSCHAPPEN

FACULTEIT WETENSCHAPPEN

Explanation system > evaluation

Objective

FACULTEIT WETENSCHAPPEN

• 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

FACULTEIT WETENSCHAPPEN

Target audience

FACULTEIT WETENSCHAPPEN

Visualization design

FACULTEIT WETENSCHAPPEN

Visualization design

FACULTEIT WETENSCHAPPEN

Visualization design

FACULTEIT WETENSCHAPPEN

Implementation > Recommender

FACULTEIT WETENSCHAPPEN

• Last.fm– Collaborative approach– Lots of data– Active users

• Last.fm API– Listening history– Neighbours– Recommendations

Implementation > Application

FACULTEIT WETENSCHAPPEN

• Chrome extension– Inject HTML into a webpage

Implementation > Visualization

FACULTEIT WETENSCHAPPEN

• D3.js– Lots of existing code– Well documented– Works in almost all

modern browsers

Evaluation > Iteration 1

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 1

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 1

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 1

FACULTEIT WETENSCHAPPEN

• Feasable?– Insight– Usability

• Think aloud / SUS• 5 Test users

Evaluation > Iteration 1

FACULTEIT WETENSCHAPPEN

• Feasable?– Yes– Rationale could be

discovered• SUS avg: 77

Evaluation > Iteration 2

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 2

FACULTEIT WETENSCHAPPEN

• Is the transformation from paper to digital successful?– Insight– Usability

• 5 test users• Think aloud / SUS

Evaluation > Iteration 2

FACULTEIT WETENSCHAPPEN

• Is the transformation from paper to digital successful?– Yes

• Issue: parallel edges are hard to distinguish• SUS avg: 79.5

Evaluation > Iteration 3

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 3

FACULTEIT WETENSCHAPPEN

• Real data– Insight: more relevant

data• Focus on usability– Option menu

• Insight• 5 test users• Think aloud / SUS

Evaluation > Iteration 3

FACULTEIT WETENSCHAPPEN

• Negative:– Threshold– Slow loading times– Distinguish between recommendations and

owned items– Learning

• SUS avg: 76.5

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

• Where changes positive?• Evaluating aims• 10 test users• Think aloud / SUS

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

• Positive:– Tension– Underlining owned items– Keeping current data in local storage

• Negative– Learning– Visual clutter when a showing approx. 40+ items

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

• SUS avg 80.5

Evaluation > Iteration 4

FACULTEIT WETENSCHAPPEN

• 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

FACULTEIT WETENSCHAPPEN

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

FACULTEIT WETENSCHAPPEN

Demo

• https://chrome.google.com/webstore/detail/soundsuggest/jimmblcjmmjjfaklclmohcnabndlidmb?hl=nl&gl=BE

• http://www.last.fm/home/recs

FACULTEIT WETENSCHAPPEN

Stats

FACULTEIT WETENSCHAPPEN

Stats

FACULTEIT WETENSCHAPPEN

Stats

FACULTEIT WETENSCHAPPEN

Thank you!

• For your attention!• Special thanks to my supervisors Joris and

Katrien!

FACULTEIT WETENSCHAPPEN

Questions?

FACULTEIT WETENSCHAPPEN