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Neural Network Homework#2 KDD CUP 2007 Task1. Student : Chao-Dian Chen M9615075. Method and System. Use Network Type : Feed- Forward  Backprop Performance function : MSE Nunber of Layer: 2 Neurous Number : 6 Select  1 to 150  training_set data  for train data.

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neural network homework 2 kdd cup 2007 task1

Neural Network Homework#2KDD CUP 2007Task1

Student : Chao-Dian Chen

M9615075

method and system
Method and System
  • Use Network Type : Feed- Forward  Backprop
  • Performance function : MSE
  • Nunber of Layer: 2
  • Neurous Number : 6
  • Select  1 to 150  training_set data  for train data
slide3
Movie_id and Customer_id for training

Customer’s rating for target

Test data for test

result
Result

The training function use TrainSGC

And Transfer function use LOGSIG

is the best function

The divergence is 0.151452

slide7
row 1 means rating is 1  training  data
  • row 2 means rating is 2  training  data
  • row 3 means rating is 3  training  data
  • row 4 means rating is 4  training  data
  • row 5 means rating is 5  training  data
  • row 6 means rating is 0  training  data
  • column 1 means that in who_rated_what_2006 dataset  the first people rating a movie( 0~5 )  < Customer ID 16983, Movie ID 6> and so on column2 , column3....
analysis
Analysis

Training parameters

  • min_grad :1e-006
  • max_fail :5
  • sigma :5e-005
  • lambda :5e-007

The result is better than others.

we want the rating between 0 to 1.

slide10
Beacause just select 150  training_set data , the total training_set  number is 17770 , there are many data no use , so the accuracy not good.
  • Just use training_set data , don't consult and use movie_ID and customer in what date to see the movie.
  • movie_ID its word has a big relation , like Dinosaur Planet, it is a series movies . if someone like this movie , he will rate the high grade for Dinosaur Planet .
  • And  if the Dinosaur Planet 2  produce , he will go to the movie and rate it .
slide11
Some key words in the movies can think  that they are the same property movies , like  the word  "war", someone like this kind movies , and he rating high grade , we can forecast  that he rates this movie about war  and has big probability to get  high grade.
  • Date plays an important role , if someone starts to rate the movie at 1990 ,then if the test data is less than 1990 , people doesn't rate that movie and so on.
  • so if want to get weight for Date , Word ,and rating , i thank Data<Word < rating .