Looking for assistance kick-starting a new machine learning scenario. In this case I need to pair one entity (ex. person) with a group of entities (ex. other people) given a history of matching patterns over time.
For example: Assume we have people who like other people with a combination of traits such as height, weight, education level, etc... This notation of 'like' is bi-directional when calculating probabilities. When a new person arrives I'd like to isolate a group of people (ranked by probability) to recommend them too.
In this case the input would be a single person with all their traits and the output would be a list of people with ranked probabilities of matching. There seems to be two main sets of data
- Known people
- Traits known people have liked or not with the traits of people they have liked or not
I am relatively new so not sure which types of training model to use. For example, is this something that can be accomplished most reasonable with a linear regression model, binary classification, boost-tree's, matchbox, etc, etc, etc..? Possibly I need a combination.
I am using the Azure Machine Learning studio which has lots of options but I am just not sure where to start in this scenario. Any suggestions or pointers would be appreciated in helping