I'm building an application which should suggest products for the users. I want to base my recommendation on different attributes, like location, weather, date, etc. Each of these attributes can have multiple values so the feature space I need to consider is huge. I was thinking about two approaches to solve this problem. Firstly, using decision trees, so I create the tables with different decisions, e.g.
sunny; hot; France; summer; choose xyz
overcast, warm, Italy, spring, choose abc
Based on this data I could learn the decision tree and use it in my application.
Secondly, I could tag every recommendation item with the possible attributes to which it applies. For example:
xyz: {sunny} {hot} {France, Spain} {spring, summer}
abc: {overcast, raining} {cold, warm} {Italy} {spring, summer}
Then, based on the actual values of the attributes from the user I could infer an item to recommend.
The second option looks better for me as it requires from me only describing the recommendation items while the first approach requires describing a lot of situations which might happen so that the decision tree is of high quality. Unfortunately, I don't know any algorithm for the second solution.
Which approach would you use? If the second one, than what are the possible algorithms to have a look at?
