Research Article
Aggregated Recommendation through Random Forests
Algorithm 1
Random decision tree.
Input: , condition attributes , decision attribute | Output: | Method: | (1) if or then | (2) leaf = the distribution of d in the | (3) return | (4) end if | (5) if (all d of are equal) then | (6) leaf = the distribution of d in the | (7) return | (8) end if | (9) is a copy of C | (10) false | (11) while (not do | (12) att = an attribute randomly selected from C | (13) if then | (14) true | (15) | (16) end if | (17) | (18) if then | (19) | (20) end if | (21) end while | (22) if (not then | (23) leaf = the distribution of d in the | (24) return | (25) end if | (26) Decision tree attribute for Root = att | (27) for (each possible value of att) do | (28) Add a new tree branch below Root, corresponding to . | (29) Let be the subset of that have the value | (30) if then | (31) leaf = the distribution of d in the | (32) return | (33) else | (34) , , | (35) end if | (36) end for |
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