recomendations.erl

%%% File    : recomendations.erl
%%% Author  : Russell Brown <[email protected]>
%%% Description : The first chapter of Programming Collective Inteligence, but in elrang, like.
%%% Created : 15 Jun 2009 by Russell Brown <[email protected]>

-module(recomendations).

-compile(export_all).

%%
%% @spec data() -> List
%% A list of prefernce data for use in the recomendations functions
%%
data() ->
    [{"Lisa Rose",  [{"Lady in the Water", 2.5}, { "Snakes on a Plane", 3.5}, {"Just My Luck", 3.0}, {"Superman Returns", 3.5}, {"You, Me and Dupree", 2.5}, {"The Night Listener", 3.0}]},
     {"Gene Seymour",  [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 3.5},  {"Just My Luck", 1.5}, {"Superman Returns", 5.0}, {"The Night Listener", 3.0}, {"You, Me and Dupree", 3.5}]},
     { "Michael Phillips", [{"Lady in the Water", 2.5}, {"Snakes on a Plane", 3.0}, {"Superman Returns", 3.5}, {"The Night Listener", 4.0}]},
     { "Claudia Puig",  [{"Snakes on a Plane", 3.5}, {"Just My Luck", 3.0},  {"The Night Listener", 4.5}, {"Superman Returns", 4.0}, {"You, Me and Dupree", 2.5}]},
     { "Mick LaSalle",  [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 4.0}, {"Just My Luck", 2.0}, {"Superman Returns", 3.0}, {"The Night Listener", 3.0}, {"You, Me and Dupree", 2.0}]},
     { "Jack Matthews",  [{"Lady in the Water", 3.0}, {"Snakes on a Plane", 4.0},{"The Night Listener", 3.0}, {"Superman Returns", 5.0}, {"You, Me and Dupree", 3.5}]},
     { "Toby",  [{"Snakes on a Plane",4.5},{"You, Me and Dupree",1.0},{"Superman Returns",4.0}]}
    ].

%%
%% @spec euclidean_similarity(Data::prefs(), Person1::string(), Person2::string()) -> float()
%% @type prefs()
%% Figures out how close Person1 and Person2 are using the Euclidean Distance Score.
%% 
%%
euclidean_similarity(Data, Person1, Person2) ->
    Person1s = proplists:get_value(Person1, Data),
    Person2s = proplists:get_value(Person2, Data),
    SumOfSquares = lists:sum( [ math:pow(X - Y, 2) || {T, X} <- Person1s, {T1, Y} <- Person2s, T =:= T1] ),
    1 / ( 1 + math:sqrt( SumOfSquares ) ).

%%
%% @spec pearson_similarity(Data::prefs(), Person1::string(), Person2::string()) -> float()
%% @type prefs()
%% Figures out how close Person1 and Person2 are using the Pearson Correlation Score.
%% 
%%
pearson_similarity(Data, Person1, Person2) ->
    Person1s = proplists:get_value(Person1, Data),
    Person2s = proplists:get_value(Person2, Data),
    {Xs, Ys} = lists:unzip([ {X, Y} || {T, X} <- Person1s, {T1, Y} <- Person2s,
			   T =:= T1]),

    N = length(Xs),

    Sum1 = lists:sum( Xs),
    Sum2 = lists:sum(Ys ),

    Sum1Sq = lists:foldl( fun(X, Acc) -> Acc + math:pow(X, 2) end, 0, Xs),
    Sum2Sq = lists:foldl( fun(Y, Acc) -> Acc + math:pow(Y, 2) end, 0, Ys),

    SumProd = lists:sum(  lists:zipwith(fun(X,Y) -> X * Y end, Xs, Ys) ),
   
    Num = SumProd - (Sum1 * Sum2 /N),
    Den = math:sqrt( (Sum1Sq - math:pow(Sum1, 2) / N) * ( Sum2Sq - math:pow(Sum2, 2) / N )),
   
    case Den of
	0 ->
	    0;
	_ ->
	    Num/Den
    end.    

%%
%% @spec top_matches(Data::prefs(), Person::string, SimalarityFun:function()) -> List
%% returns a list in order of closeness for Person against all others in Data, measured by SimalarityFun
%%
top_matches(Data, Person, SimalarityFun) ->
    Peeps = [ Peep || {Peep, _} <- Data, Peep =/= Person ],
    Similarities = lists:foldl(fun(X, Acc) -> [{X, SimalarityFun(Data, X, Person)}|Acc] end, [], Peeps),
    lists:sort(fun( {_,A}, {_,B} ) -> A>B end, Similarities).