POPULATING PRODUCT RECOMMENDATION LIST

1 . A computer-implemented method for populating a product recommendation list comprising:

identifying a first set of products using customer data and a second set of products using social network data;

identifying a third set of products, wherein the third set of products includes products in the second set of products and not in the first set of products;

calculating a product score for each product in the second set of products; and

populating the product recommendation list of the customer with a subset of the first set of products and a subset of the third set of products based on the calculated product scores.

2 . The method of claim 1 , further including identifying the first set of products and the second set of products based on an affinity score representing an affinity of the customer toward a product.

3 . The method of claim 1 , wherein the product score calculated for each product in the second set of products includes a function of measurable interactions and relationships in the social network.

4 . The method of claim 1 , further including populating the product recommendation list to a threshold number of products.

5 . The method of claim 1 , further including:

determining a set of recommended products from the first set of products and the third set of products; and

calculating the product score for each product in the set of recommended products that is in the second set of products.

6 . The method of claim 1 , wherein the customer data further includes data relating to products the customer has, products related customers have, and the customer's browsing history.

7 . The method of claim 1 , wherein social network data further includes data relating to a subset of users in the social network.

8 . A non-transitory computer-readable medium storing a set of instructions executable by a processing resource to:

identify a first set of products using customer data and a second set of products using social network data based on a calculated affinity score for each product in the first set of product and the second set of products;

identify a third set of products, wherein the third set of products includes products among the second set of products and not among the first set of products;

determine a set of recommended products among the products in the first set of products and the third set of products based on the calculated affinity scores;

calculate a product score for each product in the set of recommended products that is among the second set of products using data associated with users in the social network; and

populate the product recommendation list with the set of recommended products based on the calculated product scores for each product satisfying a criterion.

9 . The non-transitory computer-readable medium of claim 8 , wherein the instructions are executable to determine a function of a rate of success of product recommendations in the first set of products and the third set of products for the customer.

10 . The non-transitory computer-readable medium of claim 8 , wherein the instructions are executable to populate the product recommendation list based on a determined function of a rate of success of product recommendations.

11 . The non-transitory computer-readable medium of claim 8 , wherein the instructions are executable to eliminate a product from the set of recommended products based on the product having a higher product score than a threshold product score.

12 . The non-transitory computer-readable medium of claim 8 , wherein the instructions are executable to revise the set of recommended products to include a revised product in response to eliminating a product from the set of recommended products.

13 . A system for populating a product recommendation list, comprising:

a memory resource; and

a processing resource coupled to the memory resource to implement;

a first set of products and second set of products module to:

identify a first set of products using customer data based on a calculated affinity score for each product in the first set of products; and

identify a second set of products using social network data based on a calculated affinity score for each product in the second set of products;

a third set of products module to identify a third set of products, wherein the third set of products includes products among the second set of products and not among the first set of products;

a set of recommended products module to:

determine a set of recommended products among the products in the first set of products and the third set of products based on the calculated affinity scores;

eliminate a first product from the set of recommended products in response to a calculated product score of the first product being above a threshold product score and no user action; and

revise the set of recommended products with a revised product from at least one of the first set of products and the third set of products in response to eliminating the first product;

a product score module to calculate the product score for each product in the set of recommended products that is among the second set of products using social network data; and

a product recommendation list to populate the product recommendation list of the customer with the revised set of recommended products.

14 . The system of claim 12 , wherein the set of recommended products module is configured to:

eliminate a second product from the set of recommended products in response to a user not recommending the second product to the customer; and

wherein the second product from the set of recommended products is an object of a social network-marketing campaign.

15 . The system of claim 12 , further comprising a tracking module to track data associated with an effectiveness of product recommendations of products in the product recommendation list of the customer.