Introduction to Data Mining Problems

Use R to devise a book recommendation system for the data uploaded to Blackboard.  In particular, develop a system that can recommend up to three books for an arbitrary user that can be entered into R after sourcing your code.  Develop such a system using both a:

(a) User-based collaborative filtering approach. Use Euclidean, Manhattan, correlational, and cosine similarity distance measures. What problems (if any) do you run into?

(b) Item-based collaborative filtering approach. Use an adjusted cosine similarity approach as discussed in class. How does this approach compare to the user-based approach?

To load the data into R you will need to use the read.csv function.  (i.e. read.csv(filename,header=TRUE)).  Please type in ?read.csv" to the R console to see the syntax if you would like further info regarding the function's syntax.  

Make your programs functions, where the names of users, can be entered into the R prompt.  

(c) What are some general problems with both approaches? Conceptually speaking, how can these issues be ameliorated?