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Similaria is a Scala library implementing a recommendation engine, namely item-item binary collaborative filtering. In practice, it can infer knowledge like:

  • People that like this item also like...
  • Users that bought this item also buy...
  • If you loved this item you may also love these other ones...

Similaria is particularly well suited for e-commerce and other similar contexts: when a user express interests for a product (e.g. she likes/visits/pins a product) other related products can be recommended.

A Convenient Engine for "Not-Too-Big Data"

Similaria is a self-contained, centralized engine. As such, there is an upper limit - although very high - on how much load it can handle. On the other hand is very easy to setup, and it can be trained online, with no need for batch background processing. You just start it and it's ready to learn from new data and give recommendations. It is safe to use Similaria in a multi-threaded environment.

How Performant Is It?

I went through several iterations to optimize Similaria, in the effort of making it as fast as possible, effectively pushing its upper limit. My intention is to publish a more complete and meaningful benchmark and performance analysis, but for now these are my results testing it locally on a 2009 MacBook Pro with SSD. I wrapped Similaria in a thin HTTP API to serve recommendations in JSON format. I trained the engine with the LastFM 360K dataset. When asking for 10 recommended artists for people who like "The Beatles", I could reach ~3000 requests per second:

Concurrency Level:      4
Time taken for tests:   0.628 seconds
Complete requests:      2000
Failed requests:        0
Write errors:           0
Total transferred:      1296000 bytes
HTML transferred:       1058000 bytes
Requests per second:    3186.00 [#/sec] (mean)
Time per request:       1.255 [ms] (mean)
Time per request:       0.314 [ms] (mean, across all concurrent requests)
Transfer rate:          2016.14 [Kbytes/sec] received

Connection Times (ms)
              min  mean[+/-sd] median   max
Connect:        0    0   0.2      0       4
Processing:     0    1   0.5      1       5
Waiting:        0    1   0.5      1       5
Total:          0    1   0.5      1       6

Percentage of the requests served within a certain time (ms)
  50%      1
  66%      1
  75%      1
  80%      1
  90%      2
  95%      2
  98%      3
  99%      3
 100%      6 (longest request)


import com.lucaongaro.similaria._

val opts = Options(
    dbPath: "db/similaria", // Directory where to persist data (must exist)
    dbSize: 1073741824      // Maximum data size (here 1GB). Can be increased later.

val similaria = new Similaria( opts )

*  ---- Training Similaria: ----
*  Let's say that some users liked the following items (referenced by numeric
*  IDs):
*  | User | Items liked  |
*  |  1   | 1, 14, 5, 45 |
*  |  2   | 34, 3        |
*  |  3   | 2, 15, 5, 1  |
*  |  4   | 5, 1, 2      |
*  We call a set of items liked by the same user a "preference set". Similaria
*  learns by reflecting on preference sets:

val preferenceSets = List(
  Set( 1, 14, 5, 45 ),
  Set( 34, 3 ),
  Set( 2, 15, 5, 1 ),
  Set( 5, 1, 2 )

/* Tell similaria to learn this preference set: */
for ( set <- preferenceSets ) similaria.addPreferenceSet( set )

*  If at a later date user 1 also likes items 34 and 52, you can tell
*  similaria to append them to the previous preference set (provided that you
*  still know what the original preference set was):
similaria.addToPreferenceSet( preferenceSets.head, Set( 34, 52 ) )

*  Similaria also provides methods to forget a preference set or part of it.
*  Note how similaria does not know directly about users, but only about
*  preference sets.

/* ---- Getting Recommendations: ----
*  You can give an item ID to similaria, and ask for a number of recommended
*  items. These are the items that tend to co-occur in the same preference sets
*  as the given item. We call these items the "neighbors" of the reference
*  item. The recommended items are instances of the `Neighbor` case class, and
*  ordered by a similarity measure (Jaccard distance) that goes from 0 (no
*  similarity) to 1 (perfect similarity).
*  For example, if we want to get 10 recommendations for item with ID 5:
val neighbors = similaria.findNeighborsOf( 5, limit = 10 )

println("If you liked item 5 you might also like:")

neighbors.foreach { n =>
  println s"Item ${n.item} (similarity: ${n.similarity}, co-occurrencies: ${n.coOccurrencies})"

Compile the JAR

Checkout the repo and run sbt +assembly. This will run the tests and compile fat JARs for Scala versions 2.10.0, 2.10.1 and 2.10.2.


Because Similaria uses a JNI binding for LMDB, and this binding is only provided for 64bit OSX and Linux, it can only work on these platforms. Also, a general problem with sbt and JNI bindings may cause Similaria not to work in the console. When embedded in sbt projects it should work normally, but it may require a recent sbt version, and the option fork set to true.

A solution to these problems would be to use JNA instead. I am planning to work on a JNA binding for LMDB, and any help there is very welcome.


Similaria persists data using the Symas Lightning Memory-Mapped Database (LMDB), which is licensed under the OpenLDAP Public License. The Java binding is provided by lmdbjni, which is licensed under the Apache License, Version 2.0.


Scala library implementing a performant and easy to use item-based recommendation engine




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