Explanation
If you're trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No: 1/2/3, A/B/C: or Red/Yellow/Black? If so: then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00: or-999 to
999, or +_to -_, then you need to look into regression.

Explanation
In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.

Explanation : k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values

Explanation
One approach to the design of recommender systems that has seen wide use is collaborative filtering.
Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users.
A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an
"understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation