I am trying to solve a problem of a dating site. Here is the problem
Each user of app will have some attributes – like the books he reads, movies he watches, music, TV show etc. These are defined top level attribute categories. Each of these categories can have any number of values. e.g. in books : Fountain Head, Love Story …
Now, I need to match users based on profile attributes. Here is what I am planning to do :
Store the data with reverse indexing. i.f. Each of Fountain Head, Love Story etc is index key to set of users with that attribute.
When a new user joins, get the attributes of this user, find which index keys for this user, get all the users for these keys, bucket (or radix sort or similar sort) to sort on the basis of how many times a user in this merged list.
Is this good, bad, worse? Any other suggestions?
Thanks
Ajay
The algorithm you described is not bad, although it uses a very simple notion of similarity between people.
Let us make it more adjustable, without creating a complicated matching criteria. Let’s say people who like the same book are more similar than people who listen to the same music. The same goes with every interest. That is, similarity in different fields has different weights.
Like you said, you can keep a list for each interest (like a book, a song etc) to the people who have that in their profile. Then, say you want to find matches of guy
g:The choice of weights is not a simple task though. You would need a lot of psychology to get that right. Using your common sense however, you could get values that are not that far off.
In general, matching people is much more complicated than summing some scores. For example a certain set of matching interests may have more (or in some cases less) effect than the sum of them individually. Also, an interest in one may totally result in a rejection from the other no matter what other matching interest exists (Take two very similar people that one of them loves and the other hates twilight for example)