PERSONAL MUSIC RECOMMENDATION MAPPING

• US 20100328312A1
• Filed: 10/20/2007
• Published: 12/30/2010
• Est. Priority Date: 10/20/2006
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Donaldson, Justin
• Application Number:
  US12/446,326
• Publication Number:
  US 20100328312A1
• Time in Patent Office: Days
• US Class Current: 345/440
• CPC Class Codes: G06F 16/639 using playlists

Abstract

Scale free network datasets, such as music tracks, playlists and other media item recommendations are analyzed and presented in a graphic map display (FIG. 1) for visualization, preferably in an interactive environment (FIG. 2). A plotting and visualization system generally comprises a network extraction routine, coupled with a high performance eigendecomposition (map layout calculation) algorithm, and a novel visualization interaction methodology.

Claims

1. A method for analysis and visualization mapping of music data comprising the steps of:

(a) receiving a playlist comprising track ids for the corresponding tracks;

(b) accessing a recommender database or service, and retrieving a predetermined number of recommended track ids responsive to the playlist track ids, each recommended track id including respective strength metrics, the playlist ids and the recommended ids together forming a dataset;

(c) removing recommendation track ids that do not share at least a predetermined minimum number of occurrences within the dataset neighborhood, so as to reduce the dataset to a manageable proportion for visualization display;

(d) sorting the recommended track ids by popularity;

(e) retaining only a predetermined number of the overall most popular recommendation tracks to reduce the size of the dataset for visualization display;

(f) constructing a matrix from the pair-wise recommendation strengths between each pair of tracks in the reduced dataset, wherein the diagonal of the matrix is that track'"'"'s overall popularity as indicated in a selected resource;

(g) calculating a row-wise Euclidean distance across the matrix;

(h) applying a metric MDS (multi-dimensional scaling) method to the matrix to determine the predominate eigenvectors (dimensions) of the matrix;

(i) based on the predominate eigenvectors, determining a 2-dimensional map position for each of the playlist tracks and the recommended tracks; and

(j) plotting a visualization map of the reduced dataset on a graphic display screen in accordance with the 2-dimensional map position for each track.

2. A method according to claim 1 and further comprising:

calculating a natural log of each matrix element and substituting the natural log in place of the original value or each matrix element so as to compress relative distances on the plotted visualization map.

3. A method according to claim 1 and further comprising:

in the plotted visualization map, displaying corresponding meta-data for a track selected by a user.

4. A method according to claim 2 including representing the playlist tracks and the recommended tracks by different first and second symbols, respectively, on the visualization map display.

5. A method according to claim 4 wherein the first and second symbols are distinguished by different colors on the visualization map display.

6. A method according to claim 2 wherein the relative popularity of each music track is indicated by a corresponding size of the associated symbol on the map display.

7. A method according to claim 4 wherein the plotting step includes implementing a user interactive feature that enables a user to select a symbol on the map so to display meta-data associated with the corresponding music track.

8. A method according to claim 4 wherein the plotting step includes implementing a user interactive feature that dynamically repositions non-selected symbols on the map by repulsing adjacent symbols away from a current cursor location so as to alleviate occlusion in a crowded region of the map.

9. A method according to claim 8 including increasing a color saturation of a selected symbol on the map display to support user interaction with the map.

10. A method for analysis and mapping of a network dataset comprising the steps of:

(a) accessing a stored digital network dataset in which nodes correspond to individual media items, the weights of connections between network nodes indicate the strength of the connection according to at least one predetermined characteristic;

(b) retrieving a selected neighborhood subset of the network dataset so as to form a neighborhood matrix;

(c) applying a weighting function to neighborhood matrix, the weighting function selected so as to preserve the variance of each node'"'"'s edge weight distribution;

(d) applying a selected Euclidean distance calculation across the matrix so as to make the matrix symmetric; and

(e) plotting the resulting matrix data on a graphics display screen apparatus so as to form a 2-dimensional visualization map of the selected neighborhood subset of the network dataset.

11. A method according to claim 10 wherein the selected weighting function comprises the steps of:

(a) selecting a weighted adjacency matrix taken from the retrieved neighborhood for matrix A;

(b) creating a diagonal matrix D from a sum of the total connection weights for each node in the neighborhood;

(c) adding the matrices A+D to form a sum matrix; and

(d) applying a weighting function on each node of the sum matrix, the weighting function defined as by the formula

12. A method according to claim 10 or 11 wherein the network dataset comprises playlist-based music data in which the nodes correspond to individual music tracks or songs and the connection weights represent a number of times that the corresponding songs occur on a playlist.

13. A method according to claim 10 or 11 and further comprising the steps of:

(a) retrieving a second selected neighborhood subset of the network dataset so as to form a second neighborhood matrix;

(b) applying the said weighting function to the second neighborhood matrix;

(c) applying the selected Euclidean distance calculation across the second neighborhood matrix; and

(d) plotting the first and second resulting matrix data together on a graphics display screen apparatus so as to form a 2-dimensional visualization map for visual comparison of the first and second neighborhood data.

14. A method according to claim 13 wherein the network dataset comprises playlist-based music data in which the nodes correspond to individual media items and the connection weights represent a number of times that the corresponding media items occur on a playlist.

15. A method according to claim 14 wherein the first neighborhood corresponds to a user playlist and the second neighborhood corresponds to a recommended playlist created in response to the user playlist.

16. A method according to claim 15 including representing the first and second neighborhood items by different first and second symbols, respectively, on the visualization map display.

17. A method according to claim 16 wherein the first and second symbols are distinguished by different colors on the visualization map display.

18. A method according to claim 16 wherein the first and second symbols are distinguished by different shapes on the visualization map display.

19. A method according to any of claims 10-18 wherein the relative popularity of each item is indicated by a corresponding size of the associated token on the map.

20. A method according to any of claims 10-19 wherein the media items are individual music tracks or songs.