Data Hacking: Coding up a Recommendation Engine from Simple Playlist Data

  • 8 January 2016
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I love Pandora. Type in an artist’s name and it starts playing similar stuff. Pandora’s recommendation engine feels like magic.


BigQuery provides a sample data set of some playlist data (Google’s @felipehoffa says the original data set was created by @apassant, awesome data!). The data is very simple: a single row for each track in the playlist. Track data contains playlist_id, artist (id and name), album (id and title) and track (id and title).


Using this simple data, we built a recommendation engine in LookML that takes an artist, finds the most related artists and then recommends a playlist, all in about 300 lines of LookML.


View the code on Github


First, Go Ahead, Play With It


Change the filter to your favorite artist and based on this data, we’ll recommend some songs.

Click on the artist or song title to play music.



Step 1: Build Out a Simple LookML Model


The table that we’re working with is structured like this:


Table: Playlists



To build our LookML views and model, we need to build out a LookML dimension for each field in the table. Then we label each ‘object’ (things that would be in their own table in a de-normalized schema). For example, tracks.data.artist.id becomes ‘artist_id’.


  - dimension: artist_id
view_label: Artist
type: int
sql: ${TABLE}.tracks.data.artist.id
fanout_on: tracks.data

For each object, we also build a count. To count artists, we want to count the distinct values of artist_id. When drilling into an artist count, we want the artist’s id, name and the other counts.


  - measure: artist_count
type: count_distinct
sql: ${artist_id}
drill_fields: [artist_id, artist_name, count, track_count,
track_instance_count, album_count]

For grins, we build some linkage of artists to external sites so we can see their Twitter, Facebook, Wikipedia and YouTube pages, if they have them.


  - dimension: artist_name
links:
- label: YouTube
url: http://www.google.com/search?q=site:youtube.com+{{value}}&btnI
icon_url: http://youtube.com/favicon.ico
- label: Wikipedia
url: http://www.google.com/search?q=site:wikipedia.com+{{value}}&btnI
icon_url: https://en.wikipedia.org/static/favicon/wikipedia.ico
- label: Twitter
url: http://www.google.com/search?q=site:twitter.com+{{value}}&btnI
icon_url: https://abs.twimg.com/favicons/favicon.ico
- label: Facebook
url: http://www.google.com/search?q=site:facebook.com+{{value}}&btnI
icon_url: https://static.xx.fbcdn.net/rsrc.php/yl/r/H3nktOa7ZMg.ico

See the complete LookML view file


See the complete LookML model file


Learn More About the Data Set


Looks like there are about 500K playlists, with a total of about 12M tracks. There are 92K-ish different artists, with about 900K individual songs. Click on Explore Data then click on any of the numbers to drill into the data further.



















Playlists Album Count Playlists Artist Count Playlists Count Playlists Track Count Playlists Track Instance Count
213,310 92,630 504,169 901,642 12,138,977

[Explore From Here](http://looker.com/publicdata/looks/65?show=fields,data)

Who is the Top Artist (in this Data Set)?


Of course, it depends on how you count it. Which artist has the most instances of songs on playlists? Looks like Linkin Park. The really fun part of this is that after clicking Explore Data, clicking any number takes you to the album, track or artist.


Top Artists in Data Set



[Explore From Here](http://looker.com/publicdata/looks/66?show=fields,data)

Step 2: Build out Facts About What is Popular


Ranking is a great tool for building up knowledge about particular fields in a data set. The “Top 40” in a given week has long been a way of rating music.


We are going to rank tracks (songs) in their overall popularity (against all songs) and their popularity within an artist. We’d like to end up with a table like:









track_id artist_id overall_rank artist_rank

We can do this with a relatively simple 2-level query. The first level groups by track_id and artist_id and counts the number of playlists the song appears on. The second level (using window functions), calculates the overall rank of the song and the rank within (partitioned by) the artist.


 SELECT
track_id
, artist_id
, row_number() OVER( PARTITION BY artist_id ORDER BY num_plays DESC) as artist_rank
, row_number() OVER( ORDER BY num_plays DESC) as overal_rank
FROM (
SELECT
playlists.tracks.data.id AS track_id,
playlists.tracks.data.artist.id AS artist_id,
COUNT(*) as num_plays
FROM (SELECT * FROM FLATTEN([bigquery-samples:playlists.playlists]
,tracks.data)) AS playlists
GROUP EACH BY 1,2
)

We build this into a derived table and add a couple of dimensions (see the full code):


  - dimension: rank_within_artist
view_label: Track
type: int
sql: ${TABLE}.artist_rank

- dimension: overal_rank
view_label: Track
type: int
sql: ${TABLE}.overal_rank

Top 10 Songs


With these new rankings we can now see the top 10 songs in our data set.



[Explore From Here](http://looker.com/publicdata/looks/67?show=fields,vis,data)

Next, look at the ranking of the songs for each artist. We’d like more popular songs to have lower numbers. We’ve already computed rank_within_artist, let’s look at Frank Sinatra’s and Joan Baez’s top three songs. We notice that there is a data problem – there are two ids in the data for “Frank Sinatra” – but we’re just going to ignore the problem.


Change the filter to see a different artist’s top songs.



[Explore From Here](http://looker.com/publicdata/looks/68?show=fields,data)

Step 3: Find Artists that Appear Together.


We’re now ready to build the core of our recommendation engine. SQL’s cross join (cross product) will allow us to build a mapping table that will ultimately look like this:










artist_id artist_name artist_id2 artist_name2 num_playlists

To get here, we need to build an intermediate table, playlist_artist. There is a record for every artist that appears on a playlist. This intermediate table will look like this:








artist_id artist_name playlist_id

Here’s the way we write this in LookML:


- explore: playlist_artist  # Just so we can test that it works
hidden: true

- view: playlist_artist
derived_table:
sql: |
SELECT
playlists.tracks.data.artist.id AS artist_id,
playlists.tracks.data.artist.name AS artist_name,
playlists.id AS playlist_id
FROM (SELECT * FROM FLATTEN([bigquery-samples:playlists.playlists]
,tracks.data)) AS playlists
WHERE playlists.tracks.data.artist.id IS NOT NULL
GROUP BY 1,2,3
fields:
- dimension: artist_id
- dimension: artist_name
- dimension: playlist_id

Next we join playlist_artist with itself to find pairings of artists on playlists and count the number of times the parings occur. For each pair of artists, we then create a closeness ranking, by again, ranking one artist, with another based on the number of times playlists include both artists. We’ll ultimately have a table that looks like this:











a.artist_id a.artist_name a.playlist_id = b.playlist_id b.artist_id b.artist_name count(*)

And here’s how we get there in LookML:


- explore: artist_artist      
- view: artist_artist
extends: artist
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM [bigquery-samples:playlists.playlists]
sql: |
SELECT
*,
row_number() OVER (partition by artist2_id order by num_playlists DESC) as closeness_rank
FROM (
SELECT
a.artist_id as artist_id,
a.artist_name as artist_name,
b.artist_id as artist2_id,
b.artist_name as artist2_name,
COUNT(*) as num_playlists
FROM ${playlist_artist.SQL_TABLE_NAME} AS a
JOIN EACH ${playlist_artist.SQL_TABLE_NAME} as b
ON a.playlist_id = b.playlist_id
WHERE a.artist_id <> b.artist_id
GROUP EACH BY 1,2,3,4
)

fields:
- dimension: artist_id # Inherited from 'view: artist'
- dimension: artist_name

- dimension: artist2_id
- dimension: artist2_name

- dimension: num_playlists
type: int
- dimension: closeness_rank
type: int

- measure: total_playlists
type: sum
sql: ${num_playlists}

- measure: count
type: count
drill_fields: [artist_id, artist_name, artist2_id, artist2_name, num_playlists]

Now, for any given artist we can find the most closely related artists. Put another artist into the filter to find the other artists most closely related to them.



[Explore From Here](http://looker.com/publicdata/looks/69?show=fields,data)

Step 4: Mission Accomplished


For any artist, we now know their most popular songs and which artists are most closely related to them.


To recommend a playlist, we simply find the most closely related 10 artists and include each artist’s top 3 tracks.



The Code


This is the complete code to the data model.


playlist.model.lookml


- connection: bigquery_publicdata

- include: "*.view.lookml"
- case_sensitive: false

- explore: playlists
hidden: true
joins:
- join: playlist_facts
sql_on: ${playlists.playlist_id} = ${playlist_facts.playlist_id}
relationship: one_to_one
view_label: Playlists

- join: track_rank
sql_on: ${playlists.track_id} = ${track_rank.track_id}
relationship: one_to_one
type: left_outer_each
view_label: Track
fields: [track_id, overall_rank, rank_within_artist]

- explore: recommender
view: artist_artist
always_filter:
track_rank.rank_within_artist: <= 3
joins:
- join: track_rank
sql_on: ${artist_artist.artist_id} = ${track_rank.artist_id}
relationship: one_to_many
type: left_outer_each


playlists.view.lookml


# Basic playlist view 

- view: playlists
extends: [artist,track]
sql_table_name: |
[bigquery-samples:playlists.playlists]

fields:
- measure: count
type: count_distinct
sql: ${playlist_id}
drill_fields: [playlist_id]

- dimension: rating
type: int
sql: ${TABLE}.rating

- dimension: playlist_id
type: int
sql: ${TABLE}.id

- dimension: artist_id
view_label: Artist ID
type: int
sql: ${TABLE}.tracks.data.artist.id
fanout_on: tracks.data

- dimension: artist_name
view_label: Artist
type: string
sql: ${TABLE}.tracks.data.artist.name
fanout_on: tracks.data

- measure: artist_count
type: count_distinct
sql: ${artist_id}
drill_fields: [artist_id, artist_name, count,
track_count, track_instance_count, album_count]

- dimension: album_id
view_label: Album
type: int
sql: ${TABLE}.tracks.data.album.id
fanout_on: tracks.data

- dimension: album_title
view_label: Album
type: string
sql: ${TABLE}.tracks.data.album.title
fanout_on: tracks.data
links:
- label: iTunes
url: http://www.google.com/search?q=itunes.com+{{artist_name._value}}+{{value}}&btnI

- measure: album_count
type: count_distinct
sql: ${album_id}
drill_fields: [album_id, album_title, count, track_count, artist_count]

- dimension: track_title
view_label: Track
type: string
sql: ${TABLE}.tracks.data.title
fanout_on: tracks.data

- dimension: track_id
view_label: Track
type: int
sql: ${TABLE}.tracks.data.id
fanout_on: tracks.data

- measure: track_count
type: count_distinct
sql: ${track_id}
drill_fields: [track_id, track_title, count]

- measure: track_instance_count
type: count_distinct
sql: CONCAT(CAST(${track_id} AS STRING),CAST(${playlist_id} AS STRING))
drill_fields: detail

sets:
detail:
- playlist_id
- artist_name
- album_title
- track_title

playlist_facts.view.lookml


# Facts about playlists, number of different artists and number of tracks on each playlist
# Used to filter out crappy playlists.

- view: playlist_facts
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM [bigquery-samples:playlists.playlists]
sql: |
SELECT
id as playlist_id
, COUNT(DISTINCT tracks.data.artist.id) as num_artists
, COUNT(DISTINCT tracks.data.id) as num_tracks
FROM FLATTEN([bigquery-samples:playlists.playlists],tracks.data)
GROUP BY 1
HAVING num_artists > 0
fields:
- dimension: playlist_id
hidden: true
- dimension: num_artists
type: number
- dimension: num_tracks
type: number

artist.view.lookml


# Base definition for artist
# Declares external links

- view: artist
fields:
- dimension: artist_id
- dimension: artist_name
links:
- label: YouTube
url: http://www.google.com/search?q=site:youtube.com+{{value}}&btnI
icon_url: http://youtube.com/favicon.ico
- label: Wikipedia
url: http://www.google.com/search?q=site:wikipedia.com+{{value}}&btnI
icon_url: https://en.wikipedia.org/static/favicon/wikipedia.ico
- label: Twitter
url: http://www.google.com/search?q=site:twitter.com+{{value}}&btnI
icon_url: https://abs.twimg.com/favicons/favicon.ico
- label: Facebook
url: http://www.google.com/search?q=site:facebook.com+{{value}}&btnI
icon_url: https://static.xx.fbcdn.net/rsrc.php/yl/r/H3nktOa7ZMg.ico

artist_suggest.view.lookml


# Simplifed view of the top 5000 artists so we can make resonable suggestions for artists.

- view: artist_suggest
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM ${playlist_artist.SQL_TABLE_NAME}
sql: |
SELECT
artist_name
, COUNT(*)
FROM ${playlist_artist.SQL_TABLE_NAME}
GROUP BY 1
ORDER BY 2 DESC
LIMIT 5000

fields:
- dimension: artist_name


playlist_artist.view.lookml


- explore: playlist_artist  # for debugging.
hidden: true

# Simple table of playlists artist appears on. One row for every artist/playlist combination

- view: playlist_artist
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM [bigquery-samples:playlists.playlists]
sql: |
SELECT
playlists.tracks.data.artist.id AS artist_id,
playlists.tracks.data.artist.name AS artist_name,
playlists.id AS playlist_id
FROM (SELECT * FROM FLATTEN([bigquery-samples:playlists.playlists]
,tracks.data)) AS playlists
JOIN ${playlist_facts.SQL_TABLE_NAME} AS playlist_facts
ON playlists.id = playlist_facts.playlist_id
WHERE playlists.tracks.data.artist.id IS NOT NULL
AND playlist_facts.num_artists < 10
GROUP EACH BY 1,2,3
fields:
- dimension: artist_id
- dimension: artist_name
- dimension: playlist_id


artist_artist.view.lookml


# The core of the recommendation engine.  Cross joins playlist_artist to build a list of 
# related artists.

- view: artist_artist
extends: artist
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM [bigquery-samples:playlists.playlists]
sql: |
SELECT
*,
row_number() OVER (partition by artist2_id order by num_playlists DESC) as closeness_rank
FROM (
SELECT
a.artist_id as artist_id,
a.artist_name as artist_name,
b.artist_id as artist2_id,
b.artist_name as artist2_name,
COUNT(*) as num_playlists
FROM ${playlist_artist.SQL_TABLE_NAME} AS a
JOIN EACH ${playlist_artist.SQL_TABLE_NAME} as b
ON a.playlist_id = b.playlist_id
WHERE a.artist_id <> b.artist_id
GROUP EACH BY 1,2,3,4
)

fields:
- dimension: artist_id # Inherited from 'view: artist'
- dimension: artist_name

- dimension: artist2_id
- dimension: artist2_name

- dimension: num_playlists
type: int

- dimension: closeness_rank
type: int

- measure: total_playlists
type: sum
sql: ${num_playlists}

- measure: count
type: count
drill_fields: [artist_id, artist_name, artist2_id, artist2_name, num_playlists]

track_rank.view.lookml


# Rank tracks both overall and within a given artist.   

- view: track_rank
extends: track
derived_table:
sql_trigger_value: SELECT COUNT(*) FROM [bigquery-samples:playlists.playlists]
sql: |
SELECT
track_id
, track_title
, artist_id
, artist_name
, row_number() OVER( PARTITION BY artist_id ORDER BY num_plays DESC) as artist_rank
, row_number() OVER( ORDER BY num_plays DESC) as overall_rank
FROM (
SELECT
playlists.tracks.data.id AS track_id,
playlists.tracks.data.title AS track_title,
playlists.tracks.data.artist.id AS artist_id,
playlists.tracks.data.artist.name AS artist_name,
COUNT(*) as num_plays
FROM (SELECT * FROM FLATTEN([bigquery-samples:playlists.playlists]
,tracks.data)) AS playlists
WHERE playlists.tracks.data.artist.id IS NOT NULL
AND playlists.tracks.data.title IS NOT NULL
GROUP EACH BY 1,2,3,4
)

fields:
- dimension: track_id
primary_key: true
hidden: true
type: int
sql: ${TABLE}.track_id

- dimension: track_title
sql: ${TABLE}.track_title

- dimension: artist_id
type: int
sql: ${TABLE}.artist_id

- dimension: artist_name
type: int
sql: ${TABLE}.artist_name


- dimension: rank_within_artist
type: int
sql: ${TABLE}.artist_rank

- dimension: overall_rank
view_label: Track
type: int
sql: ${TABLE}.overall_rank

sets:
detail:
- track_id
- artist_id
- artist_rank
- overall_rank


3 replies

Userlevel 1

you might find this useful for loading iTunes xml into MySql



The icon links are cool… is that a forthcoming feature or an undocumented one in the current release?

Userlevel 4

Good catch @ajit! Yes indeed, the icon links Lloyd is using here are part of a new Labs feature coming in Looker 3.40. I just posted an update of what’s coming in 3.40 here.

Reply