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A Graph-Based Movie Recommender Engine Using Guile Scheme

The goal of this article is to introduce the grf3 graphdb library and its graph traversal framework named traversi. To dive into traversi, we will implement a small recommender system over the movielens dataset.

grf3 graphdb library

grf3 is graph database library built on top of an assoc space persisted to disk thanks to wiredtiger.

Basics

Basically you create vertices using (create-vertex assoc) and (create-edge start end assoc) where ASSOC must be an alist with symbol keys and any serializable scheme value as value. START and END must be <vertex> see below. Those procedures will return respectively a <vertex> and <edge> record.

Both <vertex> and <edge> have an assoc and unique identifier named uid. They have sugar syntax to interact with the assoc via (vertex-set vertex key value), (vertex-ref vertex key), (edge-set edge key value) and (edge-ref edge key). Mind the fact that -set procedures have no ! at the end which means they return a new record.

<edge> has two specific procedure called edge-start and edge-end which will return the unique identifier the start vertex and respectively the end vertex.

(get uid) will return a <vertex> or <edge> with UID as unique identifier.

(save vertex-or-edge) will persist changes done to a vertex or edge.

That is all for the basics of the grf3 library. If you experiment a little with the library in the REPL you will notice that the assoc associated with <vertex> and <edge> contains more than what you put in it. NEVER change the keys that starts with % char or the database will break.

traversi stream framework

traversi is a port of Tinkerop's Gremlin to scheme using streams implemented using delayed lambda evaluation. This does not use srfi-41 because srfi-41 is slower.

The API is similar to srfi-41:

helpers

Here is a few helpers:

Last but not least, there is (get-or-create-vertex key value) which will create a vertex with ((key . value)) if there is not vertex with such a pair or return the existing vertex otherwise.

Using a graphdb to do recommendation

Now we will put this all together to movie recommendations.

Getting started

First you need a bit of setup.

Clone the culturia repository using the following command:

> git clone https://github.com/amirouche/Culturia

Rendez vous inside the src directory, create a data directory and inside that directory download the movielens small dataset and decompress the archive:

> cd Culturia/src
> mkdir data
> wget http://files.grouplens.org/datasets/movielens/ml-latest-small.zip
> unzip ml-latest-small.zip

Go back the src directory, create a /tmp/wt directory, study a little the movielens-step00-load.scm file using your favorite editor emacs and fire guile to load the dataset as grf3 database:

> cd ..
> mkdir /tmp/wt
> emacs movielens-step00-load.scm &
> guile -L . movielens-step00-load.scm
....

Loading will take a few minutes.

To understand the graph traversal you need to know how the graph is drawn. There is three kinds of vertices:

There is two kinds of edges:

Here is a schema of the graph mapping:

graphdb mapping

Fire guile REPL and import all the things:

> guile -L .
GNU Guile 2.1.3
Copyright (C) 1995-2016 Free Software Foundation, Inc.

Guile comes with ABSOLUTELY NO WARRANTY; for details type `,show w'.
This program is free software, and you are welcome to redistribute it
under certain conditions; type `,show c' for details.

Enter `,help' for help.
scheme@(guile-user)> (use-modules (ukv) (grf3) (wiredtigerz) (wiredtiger) (ice-9 receive) (srfi srfi-26))

Open an environment over the /tmp/wt directory:

scheme@(guile-user)> (define env (env-open* "/tmp/wt" (list *ukv*)))

You are ready!

Forward

First we would like to know which movie has the id 1:

scheme@(guile-user)> (with-context env (traversi-for-each pk (traversi-map get (from 'movie/id 1))))

;;; (#<<vertex> uid: "V6HM7CEX" assoc: ((movie/title . "Toy Story (1995)") (movie/id . 1) (%kind . 0))>)

Remember that every time you access the database in the REPL you need to wrap the call using with-context. This is also the prefered way to work with grf3 in modules.

Now let's see what genres is Toy Story:

scheme@(guile-user)> (with-context env (traversi-for-each pk (traversi-map (key 'genre) (traversi-map end (traversi-filter (key? 'label 'part-of) (traversi-scatter (traversi-map outgoings (from 'movie/id 1))))))))

;;; ("Fantasy")

;;; ("Animation")

;;; ("Children")

;;; ("Comedy")

;;; ("Adventure")

Seems good to me. Mind the traversi-scatter it used just after (traversi-map outgoings ...) this is pattern that you will see very often.

Now let's do something more complex. We would like to know which movies are liked by the same people that liked Toy Story.

Remember that compose must be read from right to left, which means in this case bottom-up:

(define (users-who-scored-high movie/id)
  (with-context env
   (let ((query (compose
          ;; fetch the start vertex ie. users
          (cut traversi-map start <>)
          ;; backtrack to edge uids
          (cut traversi-backtrack <>)
          ;; keep values that are <= 5.0
          (cut traversi-filter (lambda (x) (<= 4.0 x)) <>)
          ;; fetch the 'rating/value of each edge
          (cut traversi-map (key 'rating/value) <>)
          ;; keep edges which have 'rating as 'label
          (cut traversi-filter (key? 'label 'rating) <>)
          ;; scatter...
          (cut traversi-scatter <>)
          ;; fetch all incomings edges
          (cut traversi-map incomings <>))))
      (traversi->list (query (from 'movie/id 1))))))

This is not very useful since we don't know the users, but the final result might be more interesting because we might now the movies (or at least we can STFW).

As an exercise check that the uids that were fetched are really user vertex.

Now will check the users likes and group-count to see if it makes sens:

(define (users-top-likes users)
  (with-context env
    (let ((query (compose
                  (cut traversi-group-count <>)
                  ;; retrieve movie vertex's title
                  (cut traversi-map (key 'movie/title) <>)
                  (cut traversi-map end <>)
                  ;; retrieve rating edges with a score of at least 4.0
                  (cut traversi-backtrack <>)
                  (cut traversi-filter (lambda (x) (<= 4.0 x)) <>)
                  (cut traversi-map (key 'rating/value) <>)
                  (cut traversi-filter (key? 'label 'rating) <>)
                  (cut traversi-scatter <>)
                  (cut traversi-map outgoings <>)
                  (cut list->traversi <>))))
      (list-head (query users) 10))))

The above algorithm is available in the culturia/src/movielens-step01-recommend.scm file.

See by yourself!