CS378 Final Project

1
CS378 Final Project

• The Netflix Data Set
• Class Project Ideas and Guidelines

2
The Data Set

• 17,770 Movies
• 480,189 Reviewers
• More than 100 Million reviews
• Rating of 1 through 5
• Review Date
• Uncompressed full dataset is 2 Gigabytes

3
Netflix Data Properties

• Distribution of Number of Reviews per Reviewer
• X-axis
• of reviews
• Y-axis
• P ( of reviews)

4
Netflix Data Subsets

• You will be given two subsets of the data
• Format
• Subset
• Contains 9,000 reviewers
• Restricted to only those movies with at least 5
  ratings
  
• 12,000 movies
• 2 Million reviews
• 50 MB

5
Project Requirements

• Compute each of the following
• Average review score
• Top 10 most highly rated movies
• Distribution of all review scores
• p(rating1), ..., p(rating5)
• Number of reviews as a function of time
• The reviewer whose review score distribution has
  the largest entropy
  
• Compute five other properties of the data
• These properties should be relevant to your
  project
  
• You should explain this relevancy

6
Project Options

• Classification
• Clustering
• Recommendation
• Data Cubes

7
Project 1 Classification

• Goal Predict classification scores
• 5-class classification problem
• K-Nearest Neighbor
• Represent each reviewer by a (sparse) vector of
  his review scores
  
• How can scores be predicted given a reviewer's
  nearest neighbors?
  
• Represent each movie by a vector of each
  reviewer's scores
  
• How can scores be predicted given a movie's
  nearest neighbors?
  
• Experiment with different distance measures
• Experiment with various normalization schemes

8
Project 1 Classification

• Decision Trees and other Parametric Classifiers
• Create dense features for each instance
• Reviewer's average rating
• Movie's average rating
• Movie related features
• Actors in each movie (collected from IMDB)
• Time related features
• Number of reviewer's previous scores
• Use the WEKA machine learning package
• Evaluate performance of various algorithms in the
  package
  
• Decision Tree, SVM, ...

9
Project 1 Classification

• Evaluation of Classification Performance
• Accuracy, Confusion Matrices
• Analysis Are 1's harder to predict than 5's?
• Cross-validation
• Does this make sense when these is a time-series
  component?
  
• Extensions
• Learning curves
• How does accuracy change as the training set size
  increases
  
• Distribution of accuracy per reviewer
• Are some reviewers harder to predict than
  others?
  
• Are some movies harder to predict?
• ...

10
Project 2 Clustering

• Goal Cluster reviewers and movies
• K-means based methods
• Download G-Means
• Supports k-means and also other variants
• Cluster using both sparse and dense
  representations
  
• Sparse representation same as used for KNN
  classification
  
• Dense representation same as used for parametric
  classification

11
Project 2 Clustering

• Graph-based methods
• Compute pairwise similarities between reviewers
• Correlation
• Your own ad-hoc method
• i.e. The Kevin Bacon method
• Sim(x, y) of Kevin Bacon movies viewed by
  both x and y
  
• Similarity computation may be too expensive to
  perform on the full dataset
  
• Software Graclus
• Results analysis
• Quantitative as well as Qualitative

12
Project 3 Recommendations

• Goal Create movie recommendations for each
  reviewer
  
• K-Nearest Neighbor
• Instance representation
• Sparse representation
• Find the reviewer's nearest neighbors
• Recommend movies scored highly by these
  neighbors
  
• Try out various distance measures

13
Project 3 Recommendation

• Evaluation
• Propose a way of quantifying the quality of your
  recommendations
  
• i.e. A recommendation is good if a reviewer ended
  up rating the recommendation with score of 4 or
  higher
  
• Is it harder to recommend movies to reviewers who
  do not watch many movies?
  
• Does your evaluation metric reflect this?

14
Project 3 Data Cubes

• Load the data into a data cube
• Find interesting trends in the data
• i.e. Relation between average review score and
  day of week?
  
• Slice on day, aggregate review scores across all
  reviewers and movies
  
• Find other interesting trends
• Use an open source data cube package (OLAP)
• Mondrian Java based
• Must be a proficient coder