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

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