A video describing the Netflix contest and prize.
Collaborative filtering and content-based filtering are two kinds of recommender systems that provide users with information to help them find and choose anything from books, to movies, to restaurants, to courses based on their own preferences compared to the preferences of others.
In 2009 Netflix awarded one million dollars to a group that had developed a better-recommender system than the Netflix, in-house system. This NY Times Magazine article describes the competition, the winning teams, and how the movie Napolean Dynamite caused problems for the algorithms and ranking/rating systems developed by contest participants.
In this assignment, adapated from a Nifty Assignment developed by Michelle Craig, you'll develop a program to test three different algorithms for recommending items based on the responses made by others. You'll be practicing reading data from files, using Python dictionaries and lists, and sorting data to find good matches.
The assignment comes in two conceptual parts:
We're providing two sources of data initially, but we'll solicit ratings from Duke students that you'll process as well. Sometimes ratings are stored in a single file, sometimes in more than one file. You'll need to write a separate Python module to deal with each data source, then use what these modules return to develop ratings.
This first set of recommendations for Prof. Astrachan comes from Netflix. As you can see, these recommendations are based on two movies seen and then all the data Netflix has on similar movies.
This next set of recommendations is also for Prof. Astrachan for books as reported to him from Amazon, based mostly on purchases for books in Kindle/epub format that he reads when traveling.
Detailed information is supplied in the Howto
and there will be information added to that document (or linked
here) as more data, and Duke specific data/ratings, become
available. For this assignment you will write a module
Recommender.py and two modules for reading data:
MovieReader.py. Each of these three modules is described
below and in the Howto.
get_datawhose parameters are one or more filenames. The function
get_datareturns two things: a list of items (we will call this
itlist) being rated and a dictionary of ratings (we will call this
rdict). The keys in
rdictare the names of the raters, e.g., students who completed a survey or rated things. The value associated with each name is a list of integers, the ratings for each item in
itlist. If the name "owen" is a key in
rdict["owen"]is a list of
rdict["owen"][i]the rating that owen gives to
itlist[i]. More details in the howto document.
We're providing two sets of data/ratings initially. One is book ratings. These come in two files: books.txt and bookratings.txt. The other is movie ratings. These come in one file: movieratings.txt. The formats are described in the Howto.
You should create modules
BookReader.py. The former has a method
get_data with one parameter, the latter a method with
Recommender.pyyou must import a reading module, obtain the list and dictionary from the module's
get_datamethod, and then report on at least three different ratings:
For more details on each of these, see the Howto document. You must create a module
Recommender.py that can be used to provide information
related to any ratings, but initially for either the books or
recommendedwith different values of
nfor one of the data sets to determine what items are recommended.