Long time ago I wrote a very simple Slope-One implementation (collaborative filtering algorithm): this was easy and fulfilled all my needs … which was then to learn CF and Erlang ;).

Then I realized that it could be more than fun and could even become useful as a simple recommender system. So I wrote it as an OTP application and it is published under GPLv3 license. Lots (lots!) of things still have to be done but the basic are like that:

% start application: by default use Slope-One data/algorithm
application:start(sasl),
application:start(adviserl),
% add some rating in the system
adviserl:rate(1, 2,  {3, no_rating_data}), % user 1 rate item 2 with value 3 (no data)
adviserl:rate(1, 4,  {5, no_rating_data}), % ...
adviserl:rate(2, 2,  {1, no_rating_data}),
adviserl:rate(2, 5,  {8, "damn good!"}), % any data term can be associated to rating value
adviserl:rate(3, 4,  {3, no_rating_data}),
adviserl:rate(3, 5,  {2, no_rating_data}),
adviserl:rate(3, 12, {2, no_rating_data}),
% some debug output to "see" the data
adv_ratings:print_debug(), % display the ratings per user
adv_items:print_debug(), % display a covisitation matrix
% try some predictions
adviserl:recommend_all(1), % prediction for user 1
adviserl:recommend_all(2), % ... for user 2
adviserl:recommend_all(3),
adviserl:recommend_all(4),
adviserl:recommend_all([]), % for any user without rating!
adviserl:recommend_all([{2,5}]), % for any user having those ratings
adviserl:recommend_all([{4,5}]), % idem
adviserl:recommend_all([{2,5},{4,5}]), % idem with multiple ratings
adviserl:recommend_all([{3,5}]), % ... even if item is unknown
% update on the fly
IncreaseRating = fun({R, Data}) -> {R + 1, Data} end,
DefaultRating = {1, no_data},
adviserl:rate(1, 2, {7, now()}), % user 1 change rating of item 2 from 3 to 7, adding data
adviserl:rate(1, 2, IncreaseRating, DefaultRating), % update from 7 to 8 with function
adviserl:rate(1, 42, IncreaseRating, DefaultRating), % rate item 42 at 1 (default)
ok_lah.

Among the main points on the pseudo roadmap:

  • API to call adviserl functions through process messages
  • data persistence
  • data distribution
  • algorithm distribution
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