User based recommendation factoring in user data The question is:
what algorithms (and libraries) should i use if i want to build a recommender system with the following data in mind representation:

USER_ID  ZIP  Movie1  Movie2  Movie3
1  2483  5  0  3
2  2483  4  1  5
3  2345  3  1  5


Basically i want to factor in user data into a recommendation of a movie.

(zips can be transformed to long/lat but thats another question and out of scope now)

i was searching the internet for hours with no success. So i will be grateful if someone can point me in the right direction.
Collaborative Filtering:

Match users to people with similar tastes –recommend what they like
Commonly used in e-retail
Avoids the issue of users only being recommended more of what they already like (allows serendipity)

Example:
Your Scenario.
Methods: Euclidean Distance, Cosine Distance, Pearson Correlation Coefficient (most common).
Content-Based Recommendation:

Match users directly to products and content
Recommend based on what you have bought or viewed in the past
Commonly used for document recommendation: webpages, news articles,
blogs etc.

Examples:
Which Movie(s) might our user like?
Option1: The user selects preferences for the various features using pull-down menus etc. We match against the movies using Vector-Space methods (described next)
Methods: Vector-Space Method, K Nearest Neighbour(kNN)
Option2: The user rates a sample of the movies (explicitly or implicitly) as like/dislike. we then build a user profile model for that user using machine learning.
Method: Decision Trees