For the recipe app I want to do something slightly different, showing the absolute search result quality for each result. In other words: If the best search result in a result set is not very good, make it smaller than if it has higher value, thus showing an absolute value for each result. The dialogue equivalent comparing i.e. two movies would be to define the best of them better than the other, but not the best you’d seen.

Absolute search result quality

aka. Absolute visual relevance hierarchy

How do we then measure absolute quality? In an earlier post I described what would be our relevancy hierarchy. The more in-season ingredients in a recipe, the better quality.

I’ve defined three quality groups for absolute search result quality so far (number of ingredients: n):

1. n >= 4
2. 1 < n < 4
3. n = 1

Here are three different examples on search results set. First has two results with four or more in-season ingredients and second has only one. Third has none, typically something that would happen during the winter months in Norway:

The recipe app is work in progress. Check back every now and then for new blog posts on the subject.

One generic tool for getting the right results on top is the “term frequency–inverse document frequency“, or tf-idf for short. It’s a combination of how often a term is mentioned in a document compared to how often it’s mentioned in all of your documents in the index. So, rare terms within the whole index used often in one document makes it a good search result when searching for that term. But most likely, not good enough. You need to figure out what’s the characteristics of your content, and what are the most characteristic use cases and user stories for your users. Only then can you achieve great relevancy, …  I mean get the right result on top of your search result.

Model for relevance tuning

We’ll use our Recipe app as an example…

So, for our food recipe app, we have some obvious content characteristics:

• The more ingredients in-season for one recipe is good. We’re doing an OR-search on all ingredients in-season so this comes out-of-the-box … almost.
• Quite a lot of recipes doesn’t stand the test of time. We know that most of the recipes at oppskrift.klikk.no from 2008 or newer are quite good and have nice photos.
• We’re not sure if we need this, but we know whom of the writers to trust. This may be an overkill when we already have a boosting on newer recipes.

And we know a lot about our users as well:

• Most grown up people in Norway have a job, thus limited time to prepare a meal. This means that recipes that takes shorter preparations should be boosted from Monday through Thursday. The verdict on Friday is still not decided.
• During the weekend people have more time to make dinner. The recipes that takes a short time to prepare most probably cut some corners, and are not that good compared to recipes that takes a little longer time. So for the weekends, we should do a demotion of really quick recipes, at least for dinners.

This is the info we’re going to use to sort our search result. But we have more knowledge about our users that we can use to auto-set filters for certain times of the day:

• Most work days, people don’t plan a breakfast meal or lunch. The whole day we can auto-set the main “course” filter.
• During the weekend, people may also plan a lunch. We’ve decided to auto-set the “light meal”-filter during weekends up until lunch time. After that the “main course” filter is auto-set. We’ll log if the first thing our users do is to set another filter.
• On Friday and Saturday a lot of Norwegians drink beer, wine or liquor. After some hours of drinking, they get hungry. Maybe we should have an “afterparty, quick and greasy and tasty-meal”-filter auto-set for late Fridays and Saturdays?

So, what’s the filters we’ve decided on:

• Light meals
• Starters
• Main courses
• Deserts
• … and maybe the Afterparty-thingy

What do you think?

Sounds nice? This is work in progress, so check back every now and then for new blog posts.

User driven content and search

The 1-9-90 principle makes you understand who you have to build for: The super user.

Check out the presentation on LinkedIn: “Ideas on how to find a movie to watch“.

Thanks to Harald, Johannes and Tone for feedback and valuable input.