[catalonia]

Like the mood option - very helpful. I personally also like it when a recommendation engine tells me why it thinks I will like the movie/restaurant/whatever. That way i can decide whether the recommendation makes sense for me instead of just trusting the "secret sauce". Thoughts on this?

[lizxrice]

It's really interesting the extent to which some people want to 'see the working' behind recommendations, while for others it's just clutter. I think some people want to build the mental model of what's going on, where others want it to feel like 'magic' (when it works, at least!)

I think my gut reaction against it is that people might reject a perfectly good recommendation because they didn't like the reasoning. For example, if we recommend something to Alex because Bob likes it, but Alex doesn't trust Bob's judgement, then Alex might reject the recommendation even if in practice she always enjoys the same things as Bob, she just doesn't realise it.

On the other hand, collecting feedback about whether the user agreed with the way the recommendation is made might be helpful in improving the way it works.

Definitely something to think about though.

[catalonia]

Agreed - Different users will have different reactions. Some will want to see the reasoning at least when they have just started using the service, just to see if the service works for them, while for others, it might be clutter. I like how Nara's iOS app does this (although it too doesn't do it perfectly).. they show you the recommended restaurants for you. When you click on any one result, they have a tab which shows you why they recommended it. That way, its there for the users who are struggling to make the leap of faith but not in the way of those who already trust the recommendation.

[catalonia]

Also good point about collecting feedback about how users react to the way the recommendations was made. You could have different algorithms generating recommendations for different users depending on this e.g. collaborative filtering for some users, neural network for others, ensemble for the rest etc.