// purchase product and return relevant product recommendations
public List<Product> purchase(Customer customer, Product product) {
purchasesCache.put(customer.id, product.id);
ProductAssociativityGraph graph = productAssociativityGraphMap.get(product.category);
Vertex v = Vertex.create(product.id);
List<Vertex> associations = graph.getProductAssociations(v);
int recommendSize = Math.min(associations.size(), maxNumRecommendations);
return associations
.stream()
.map(vertex -> productCache.get(vertex.productId))
.limit(recommendSize)
.collect(Collectors.toList());
}
// batch job (possibly real-time using streaming APIs)
public static void createProductAssociativityGraphPerCategory() {
Set<String> customers = purchasesCache.keySet();
customers.forEach(
customer -> {
List<String> customerPurchases = Lists.newArrayList(purchasesCache.get(customer));
for (int i = 0; i < customerPurchases.size(); i++) {
for (int j = i + 1; j < customerPurchases.size(); j++) {
Product product1 = productCache.get(customerPurchases.get(i));
Product product2 = productCache.get(customerPurchases.get(j));
if (product1.category.equals(product2.category)) {
ProductAssociativityGraph graph =
productAssociativityGraphMap.getOrDefault(
product1.category, ProductAssociativityGraph.create());
graph.addAssociation(Vertex.create(product1.id), Vertex.create(product2.id), 1);
productAssociativityGraphMap.putIfAbsent(product1.category, graph);
}
}
}
});
}