Transparency and control is not the pinnacle of user experience design for recommender systems. Our ability to scrutinize the recommendations given to us by the system does not unanimously increase our ability to effectively choose the right option. Transparently knowing all the pros and cons of every recommended options may actually decrease our satisfaction with the choices we end up making.
Recommendation technology is not perfect, so it’s futile to expect perfect, pin-point recommendations from Amazon. When the system is not fit to make decision, we have to transfer control to the user. And when the users have to make decisions, they have to base those decisions on information. Knowing the limitations of technology, we design transparent and controllable recommender systems. But we shouldn’t allow this user experience hack to cloud our vision of what a better recommendation experience may be like.
How can more information and more options be bad for you? Will it not make you better at defining your goals, to evaluate how likely each of the options is to meet your goals, and finally to choose a winning option? As I wrote about in my previous post (and of which Barry Schwartz is the true source), an overload of both options and information about those options will ultimately lead to the Paradox of Choice and regret over missed opportunities.
Daniel Tunkelang argues well for transparency in information retrieval:
The idea of transparency is simple: users should know why a search engine returns a particular response to their query. Note the emphasis on “why” rather than “how”. Most users don’t care what algorithms a search engine uses to compute a response. What they do care about is how the engine ultimately “understood” their query–in other words, what question the engine thinks it’s answering. [...] But what frustrates users most is when a search engine not only fails to read their minds, but gives no indication of where the communication broke down, let alone how to fix it. In short, a failure to provide transparency.
He also ties it in with query elaboration as a dialogue:
The answer is that the system needs to help the user elaborate the query. Specifically, the process of composing a query should be a dialogue between the user and the system that allows the user to progressively articulate and explore an information need.
Sometimes your goal is not to collect information, but to reach a good decision with the smallest possible amount of effort. Like when I go to the coffee shop to get my daily cup of black sunshine. Usually they have two blends of black coffee to offer. I ask the girl behind the counter what her favorite is. She says “classic espresso roast”, and that is what I choose for myself that day. I don’t care why she likes that particular blend, where the beans come from, or what the guy next to me thinks. I just made an efficient and satisfactory decision, uncontaminated by second-guessing and opportunity costs.
Not all choices in life are as trivial as this, and I can’t confront all decisions with this blind simplicity. As stakes get higher, bad decision become more fatal, and I should safe-guard myself by investing more time in the decision-making process. But I still believe that transparency and control are slightly over-rated quality measures for online recommender systems, considering the relatively trivial nature of shopping for entertainment and produced goods. More focus could instead be placed on trust, efficiency (making faster decision) and overall subjective satisfaction with the end result. I have no reason to scrutinize the person recommending me a blend for my daily cup of coffee, and I’m oblivious to the taste criteria factored into the recommendation. I trust the recommendation to be good enough for me, end of story.
I agree with Daniel again, when he writes:
If the user is satisfied with one of the top results, then transparency is unlikely to even come up. Even if the selected result isn’t optimal, users may do well to satisfice. But when the search engine fails to read the user’s mind, transparency offer the best hope of recovery.
I may indeed do well to satisfice, and recommender systems should help me satisfice quickly and with confidence. And if that fails, transparency is there to help our relationship back on track.
Update: I have an idea for a different kind of recommender user experience that I will share with you next time. See you later!