Awesome

Birdland is a famous Jazz club. It is also a recommendation library.

Birdland is a collaborative filtering algorithm in two steps: exploration and recommendation. To explore possibilities the algorithm performs a random walk on the (biaised) user-items bipartite graph starting from a list of items provided as an input. This random walk generates a list of (user, item) pairs that are processed by the recommender which returns a list of recommended items.

Birdland has some advantages over other collaborative filtering algorithms:

• It requires no pretraining.
  Most collaborative algorithm come with hidden costs. Not only do you need to maintain an extra service and database, you also need to solve an additional problem: find the N nearest neighbors of a vector (spoiler: it is not an easy problem).
• It is fast.
  We achieved performance of the order of the millisecond on an API serving recommendation of millions of items for a million users.
• It is simple to reason about, thus to customize.
  To build Bird we started from the simple question: how would I look for new music to listen? Back in the LastFM days I would look for users who had listened to similar artists, what they've listened to etc. and trust more users who had very similar tastes. Bird does exactly that, but a million times faster than you would. There is something you do not like about this story? Well, you can adapt Bird, or use Emu.
• It generalizes to a social recommender.
  Weaver uses the social network between users to inform recommendations.
• It recommends both items and users in one pass. No need to find the N nearest neighbors again.
• It solves the long-tail problem for a specific set of parameters.
  (Blog post to come) Now, whether this is desirable or not is another debate.
• It is ready for production.
  Birdland has been tested succesfully in production. Import birdland in the service that implements the recommendation API, plug in the data and you're all set.

The codebase is organized around the following components:

samplers

• tower_sampler.go implements the tower sampling algorithm to sample from a discrete distribution;
• alias_sampler.go implements the alias sampling algorithm to sample from a discrete distribution.

explorers

• bird.go implements a simple recommender engine based on a user-item graph;
• emu.go is a recommender engine based on a user-item weighted graph;
• weaver.go is a recommender engine based on the user-item bipartite graph and the user-user social graph.

recommenders

• recommend.go contains the functions used to produce recommendations from the engines.

Engines

Bird

Named after Charlie "Bird" Parker.

The very first step is to map the users and items to sets of consecutive integers (starting with 0). This avoids working with maps, which substantially improves performance.

Initialize the engine with a list of item weights, and the (user, item) adjacency table:

package main
import "github.com/rlouf/birdland"

artistWeights := make([]float64, numArtists) // global weight attributed to each artist
usersToArtists := make([][]int, numUsers) // for each user the list of artists they listened to (liked, followed, etc.)
cfg := NewBirdCfg()

bird, err := birdland.NewBird(cfg, artistWeights, usersToArtists)

This needs to be done only once (provided your data do not change). The engine processes queries---lists of (artist_id, weight) pairs---and outputs a list of artists and their referrers:

query := []QueryItem{} // QueryItem{Item int, Weight float64}
items, referrers, err := bird.Process(query)

We can then use items and referrers to recommend either artists or referrers (see the "Recommenders" section below). All engines depend on two parameters:

• the depth of the random walk;
• the number of random walks that are performed (number of samples drawn from the query).

they can be tuned by initializing the configuration passed to NewBird by hand:

cfg = BirdCfg{Depth: 2, Draws: 10000}

Emu

The emu is a heavy bird (the 5th heaviest).

Emu works very similarly to Bird. The only difference lies in the initialization; instead of taking a simple bipartite graph [][]int as an input, Emu takes a weighted bipartite graph []map[int]float64. In the context of music recommendation, the weight can for instance be the number of times the user played tracks from an artist.

package main
import "github.com/rlouf/birdland"

artistWeights := make([]float64, numArtists)
usersToWeightedArtists := make([]map[int]float64, numUsers)
cfg := NewBirdCfg() // Default of 1000 draws and depth 1

emu, err := birdland.NewEmu(cfg, artistWeights, usersToWeightedArtists)

Everything else is exactly the same.

Weaver (cleaning)

Weavers are allegedly very sociable birds.

The same way Emu attributes different weighs to each item, Weaver attributes different weights to each user. This follows from the idea that you would not weigh recommendations by strangers and by acquaintances the same way.

package main
import "github.com/rlouf/birdland"

cfg := NewWeaverCfg()
itemWeights := make([]float64, numItems)
usersToItems := make([][]int, numUsers)

weaver, err := birdland.NewWeaver(cfg, itemWeights, usersToItems, socialGraph) 

We give to users who are not connected to the current user a default weight of 1. This default behavior can be changed by initializing the configuration by hand:

cfg := WeaverCfg{DefaultWeight: 0, BirdCfg: NewBirdCfg()}

which would only consider recommendations coming from direct connections.

Recommenders

Since the engines traverse both users and items, we can recommend one or the other (or both) indifferently within the same query. Birdland provides several functions to produce recommendations from the engines' outputs.

Two functions were defined to provide a stable interface for the services that use Birdland and so strategies could be swapped without affecting said services. You can consult recommend.go to see the available strategies.

recommendedArtists := birdland.RecommendItems(items, referrers)

Produces an ordered []int that contains the id of the recommended artists.

recommendedUsers := birdland.RecommendUsers(items, referrers)

Produces an ordered []int that contains the id of the recommended users.

Contribute

Questions, Issues or PRs are very welcome! Please read the CONTRIBUTING.md file first, then happy forking.

Credits

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