This is a typical recommendation problem. Major online dating sites are using graph databases like Neo4j to solve that recommendation problem. It helps them suggest in real-time potential dates to their customers.
Effective Dating Series Part I - The Problem
The better the suggestions, the more chances people will want to meet…and enjoy doing so. We are going to see how to do recommendation with graphs. For this we will use a online dating example. Of course, the same approach could be applied to other domains like retail.
In order to show how to use graphs to write recommendation algorithms, we are going to use a fake dataset.
Online dating and graphs : a love story?
It emulates the kind of data an online dating site would have. Here is a quick overview of the underlying data model:. The graph is centered around the people. People are linked to the locations where they live in and to attributes. People can have two kinds of relationships with an attribute: At this point what we have done is simply express the data in a way that makes sense. What we want is to find good matches between people.
The data model we have is going to help us do that. It illustrates a simple truth: So what kind of partners are people looking for? The general picture is unsurprising. In particular, women tend to deviate much further from their stated preferences than men. However, they are much less flexible than men about marital status and number of children. These choices are indistinguishable from random selections, say Xia and co.
Building a Dating site with Neo4j – Part One
Nevertheless, the general trend is that people tend to reply to others who match their stated preferences. Of course, there are likely to be significant differences between certain aspects of dating behavior in China compared to other parts of the world. For example, there is very little ethnic variety in China— Who is Dating Whom: Mainstream online legal services are getting serious about using crypto to automate bits of what they do—and lower the bar to entry for us all.
Many thought the internet would bring democracy to China. Instead it empowered rampant government oppression, and now the censors are turning their attention to the rest of the world.
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A Dating App Data Model
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In the next article of the series I will present the impact and pitfalls of the Effectively Dated design above and how to overcome them. We will build on the simple data model that we started with here and see what issues surface as you take Effective Dating further and further. In the final step of Database Design, Joe Celko gives a simple but effective explanation of the norm Daily SQL Articles by email:. Stay up to date: Daily newsletters with brand new articles, scripts, editorials and a Question of the Day help you keep on top of SQL Server.
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Improve your SQL Server knowledge daily with more articles by email. Thank this author by sharing: The Concept Although we don't always express this in our database schemas, most data is temporal; it actually only lives over a period of time. Below is a simple diagram of this schema: The Problem So what's the problem here? Let's illustrate how this design painfully falls apart: Wrong, by several counts: Once you accept that you have to run a batch job, things start to go downhill from there: What if management wants to start a promotion at midnight?
You'd better be up and watching in case that batch job fails.
Dating Sites Data Model
If the batch job does fail, you're dealing with downtime or bad pricing, both of which result in a direct cost to the business. Because you are deleting the promotion as soon as it's over, you don't have the capability to analyze how many orders the promotion generated, or do any other kind of analysis. Someone will probably get the idea that they should store this information off in an Excel spreadsheet somewhere so they can report it to management. You have now created an unnecessary business process and a questionable source of data to base management decisions on.
Every time you do a promotion you have to set up a new batch job in order to insert the data. So there is manual intervention involved with every promotion setup, which means that every promotion turns into a development project, with development and testing phases. It's a simple project, but a project nonetheless. This results in the following sorry list of circumstances: After a few failures, management will be frustrated and start asking the question "why can't we get this right?
The business will undoubtedly be slowed down in its ability to do promotions and could be outrun by the competition in this regard. Forget about the possibility of doing many promotions at once The Solution Now, let's see what happens if we break down, repent, and just accept the fact that these promotions live and die by dates.
Here is the new database schema: Using this design, let's look at the weaknesses of the previous design and see what's happened to them: No batch job needs to be run. The system is date-aware and automatically displays the right price at the right time. So, no downtime is possible from a Development standpoint, and no staying up at night: The Development Department doesn't even have to be involved.