March 7, 2016

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 Looker’s 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:

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

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.

Explore From Here

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

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

Explore From Here

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

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

Step 5: 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.

- connection: bigquery_publicdata - include: "*.view.lookml" - 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

# 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

# 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

# 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

# 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

- 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

# 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]

# 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

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