Approach to identify percentage of matches between two data sets

I need guidance in identifying appropriate approach to solve this problem:

i have 100 people list who bought product A,B,C. Each person has a profile for instance age, gender. So the list is : [24,F,A], [25,M,B],[23,F,A],[23,F,A],[24,F,A],[24,M,C]... Now 11th person checks in the system to buy a product eg. C. His gender is F and age 24([24,F,C]).

I want to show the person that how many people with exact his/her profile match and have bought item C.

What approach should i apply? Solution 1 I tried was to store data in an excel/csv and find association rules using Apriori algorithm in a java program using Weka java API. but it shows association rules for all possible combinations. ie.( 24->A), (A->24), (24,F)->A,(A,24)->E..etc with their support and confidence values. Rather i only want rule (24,F)->A and its confidence and support. So basically i do not know how to filter specific rules using Weka Java API.

This is one approach i found but do not know if it is appropriate or there exists other approaches to ind matches in this problem?