At Conversify[0] we literally tested these methods last week(!) We help e-commerce companies with machine-learning optimizations of which a recommendation system is one. The average shop uses the standard tools for recommendations which are terrible, with some intelligence it could be a very important way to increase sales and conversion. [/end-plug]
This is what we learned. We've tested three approaches (all on sites with enough traffic):
(1) CF based on buys in combination with/without profiling
(2) CF based on purely clicks in combination with/without profiling
(3) Text analysis (sklearn, TF-IDF)
We had the best hopes for buys then clicks and as last text-analysis. It was exactly the other way around ;-).
(1) Buys has the drawback that the relationship of profiles to products is a lot to a few most of the time (relatively).
(2) Clicks has the same drawback but we can also see clicks within one session. This makes clicks somewhat better because shoppers often compare multiple products that are alike. This is valuable info.
(3) We didn't think that TF-IDF had evolved the last couple of years this much that we now can do an analysis with a few a lines of code with these results. They were stellar. We tested the outcomes with the business owners as well as customers (we didn't say which method generated which results) and they all went massively for text-analysis.
Here's our outcome for a random product from one of our customers (with a combined buys/clicks recommendation): http://i.imgur.com/lEYYIh9.png
That being said: we are working on a combination of all three methods. We think that text-analysis is for the first filtering of related products and that the CF methods are for ordering of this set. Also using weights for the different methods are useful.
Besides that we are developing a way to distinguish in an automated way between substitute products (samsung vs iphone) and complementary products (charger with a phone). This could be accomplished by using more CF than text (and specifically bought products within sessions). Our first results are promising.