User based recommendation factoring in user data
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