Dynamically Personalized Product Recommendation Engine Using Stochastic and Adversarial Bandits

1 . A method for recommending products to a user, the method comprising the steps of:

providing a user profile with product related data;

generating at least one bandit to model product related recommendations;

passing the bandit model to a recommendation module that provides recommendations to the user based on the bandit model and expected payoff; and

evaluating user interactions in response to the recommendation to adjust further recommendations.

2 . The method of claim 1 , wherein the user profile data is derived at least partially from at least one of product related user data and traffic-based link data.

3 . The method of claim 1 , wherein the bandit is an adversarial bandit.

4 . The method of claim 1 , wherein the bandit is an adaptive adversarial bandit.

5 . The method of claim 1 , wherein the bandit is an stationary adversarial bandit.

6 . The method of claim 1 , wherein the bandit is a federation bandit.

7 . The method of claim 1 , wherein the bandit is a tuning bandit.

8 . The method of claim 1 , wherein the bandit uses a reward functions based on reciprocal rank.

9 . The method of claim 1 , wherein the bandit uses a reward functions based on similarity score.

10 . The method of claim 1 , wherein the recommendation module provides dynamic personalization.

11 . A method for dynamically recommending products to a user, the method comprising the steps of:

receiving a request for a personal recommendation;

weighting a bandit payoff;

assembling bandit recommendations;

providing recommendations to the user; and

evaluating further user interactions in response to the provided recommendation to adjust weighting of the bandit payoff.