sequence mining for time and product prediction

Question

I am facing a tricky problem about sequence mining, say I have 10 products, I have millions of records each containing user, product and timestamp of purchase . Each user may have only 1 record or 100 records.. such as :

user 1, p1, t1
user 1, p1, t2
user 1, p2, t3
user 1, p3, t4
user 1, p1, t5
user 2, p2, t6.....

Now I need to predict when it's the best time to promote a product for a user.

So far, my solution is, clustering the time into a few categories. Then apply Apriori on the data, e.g the records will be like

user 1, p1T1
user 1, p2T2
user 1, p3T2
user 1, p2T1...

Then I will get rules like p1T1->p2T2 etc, because T3>T2>T1... any rules do not fit this condition will be discarded.

However, I am not very satisfied with this solution. Any suggestions?

Answer 1

Instead of applying Apriori, you could apply a sequential pattern mining algorithm (e.g. PrefixSpan, SPAM, GSP) or a sequential rule mining algorithm.

You can check my website for open-source Java source code for these algorithms and some examples:

http://www.philippe-fournier-viger.com/spmf/

Hope this helps,