Recommendation - item based vs user based
It depends on what data you are using to calculate similarity between your items.

If you are using data from a user interaction with an item - like viewing a web page or buying an item - then each time there is a user interaction with an item, the similarity between it and other items will change. If you are doing this calculation offline, then your model won't take that change into account until the next time the data set is updated and your recommendations are recalculated.

However, if you are using item meta-data to calculate similarity, then user behavior won't make any difference. For example, if you took the number of shared tags between two items, or the number of shared inbound links, etc. 

I have always used user browsing data to build content recommendation engines, but it is certainly possible to calculate similarity in many different ways. Have fun!