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The PartyVote Music Library Visualization System No play list, no DJ, no problem! Nadia Rashid, David Sprague, and Fuqu Wu.

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Presentation on theme: "The PartyVote Music Library Visualization System No play list, no DJ, no problem! Nadia Rashid, David Sprague, and Fuqu Wu."— Presentation transcript:

1 The PartyVote Music Library Visualization System No play list, no DJ, no problem! Nadia Rashid, David Sprague, and Fuqu Wu

2 Motivation

3 Previous Literature Jukola Jukola Pandora Pandora MUSICtable MUSICtable

4 Visualization Goals 1. Co-present music collaboration for closely knit social groups 2. Lightweight 3. Enable system understanding 4. Optimal for participants

5 System Usage participants participants 500+ songs 500+ songs 6 hours of non-repeating music. 6 hours of non-repeating music.

6 General Overview

7 Voting & Music Selection All songs start with weighting of 0 All songs start with weighting of 0 Participants vote for a song/album or artist Participants vote for a song/album or artist Weight = Weight + (1/# of songs) Weight = Weight + (1/# of songs) Similar songs also affected by votes. Similar songs also affected by votes. High dimensional cluster/hull defined by songs with weight > 0 High dimensional cluster/hull defined by songs with weight > 0 Songs in this cluster are potentially played. Songs in this cluster are potentially played.

8 Vote Clustering

9

10

11 The Interface

12 Interface part 2

13 Challenges MDS and Convex Hull/Clustering Algorithm. MDS and Convex Hull/Clustering Algorithm. Lots and LOTS of coding Lots and LOTS of coding Evaluation and distance metric tweaking Evaluation and distance metric tweaking

14 Visualization Goals Revisited 1. Co-present music collaboration for closely knit social groups ✔ 2. Lightweight ✔ 3. Enable system understanding ✔ 4. Optimal for participants ✔


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