For most recommendation UIs, you would need a hero item that make people want to click on. It might turn out that another vacuum is probably the best item for some people to click on, and go on to buy other stuff once they are on the site.
For most recommendation UIs, you would need a hero item that make people want to click on. It might turn out that another vacuum is probably the best item for some people to click on, and go on to buy other stuff once they are on the site.
A different recommended might use different types of conditionals (items bought instead of items looked at, for example), and also have success in different areas (like recommending iPhone cases for iPhone owners). In order to converge the models in a Bayesian framework you'd have to deal with the combinatorial explosion of products and event conditionals which might be pretty gnarly. But some convergence work would be better than none, otherwise you end up with 20 different recommender widgets on a page.
Overall I don't think amazon's approach to date has been bad...it's just time to clean up a bit.