Product recommendations are based on other customers who have purchased similar products. Collaboration comes from matching users’ preferences/confidence to people with similar tastes. Filtering is making predictions of a user’s interests/similarities by collecting preferences from other users similar to their own preferences.
The general idea is that if A purchases a similar product to B, then A is more likely to purchase an item B has purchased compared to a random person. Each user has their own unique predictions derived from information from many other users.
Collaborative filtering requires users to be active customers. If customers only purchase one distinct item it may be difficult to recommend other products. There is also a “cold start” problem, which occurs when new customers or items arrive and there's no purchase data for them. In this case, we can recommend popular items. Popular items are also recommended and can be used as a baseline to evaluate the usefulness of collaborative filtering recommendations. With collaborative filtering, Kurvv can help increase sales by recommending products customers are likely to purchase
Kurvv’s collaborative filtering uses the Alternative Lease Square (ALS) algorithm. You can read about ALS here
Sample Output...
