System and method for generating an alternative product recommendation

1. A method implemented by one or more computing devices for automatically generating a naturally reading narrative product summary including assertions about a specific product selected by a user, said method comprising:

determining, by at least one of the one or more computing devices, at least one attribute associated with said specific product;

selecting, by at least one of the one or more computing devices, an alternative product based on said at least one attribute; and

generating, by at least one of the one or more computing devices, a naturally reading narrative including assertions about the specific product and a recommendation of the alternative product.

2. The method of claim 1 , wherein said alternative product has a price that is at least as high as price of said specific product.

3. The method of claim 1 , wherein a manufacturer of said alternative product is the same as the manufacturer of said specific product.

4. The method of claim 1 , wherein a manufacturer of said alternative product is different than a manufacturer of said specific product.

5. The method of claim 1 , wherein said narrative includes at least one sentence recommending said alternative product.

6. The method of claim 1 , wherein said recommendation is an advertisement, and further including the step of receiving compensation in exchange for recommending said alternative product.

7. The method of claim 6 , wherein an amount of said compensation is based on at least one of frequency of said recommendation and number of recommended products sold that is attributable to said recommendation.

8. The method of claim 1 , wherein the naturally reading narrative is generating using assertion models that comprise a plurality of assertion templates, each assertion template including a natural sentence pattern and at least one field corresponding to said at least one attribute.

9. The method of claim 1 , wherein said at least one attribute is a plurality of attributes associated with said specific product and said alternative product, said method further comprising assigning importance ratings to each of said plurality of attributes.

10. The method of claim 1 , further comprising designating at least one of said plurality of attributes as a key attribute, and wherein said generated naturally reading narrative is regarding said key attribute.

11. The method of claim 9 , further comprising determining attribute ranks for each of said plurality of attributes, and processing said attribute ranks together with said importance ratings to derive a severity value for each of said plurality of attributes.

12. The method of claim 11 , wherein said deriving said severity values includes applying an inflection algorithm.

13. The method of claim 11 , wherein said selecting said alternative product includes identification of a plurality of alternative products from a plurality of candidate alternative products.

14. The method of claim 13 , further comprising determining a severity differential between each of said plurality of attributes for said plurality of candidate alternative products and said specific product.

15. The method of claim 14 , wherein said alternative product is selected based on at least one of a predetermined near-price margin, a predetermined near-rank margin, and a predetermined severity differential threshold.

16. The method of claim 15 , wherein said selecting said alternative product includes identifying a candidate alternative product which: is within a predetermined near-price margin of said selected product; has at least one attribute with a severity value that is above a predetermined severity differential threshold; and has the same primary scenario as said selected product.

17. The method of claim 15 , wherein said selecting said alternative product includes identifying a candidate alternative product which: is within a predetermined near-rank margin for said key attribute and for all attributes of said specific product that are above a predetermined high severity threshold; is priced lower and outside a predetermined near-price margin; and has the same primary scenario as said selected product.

18. The method of claim 15 , wherein said selecting said alternative product includes identifying a candidate alternative product which: has at least one attribute with a severity value that is above a predetermined severity differential threshold; is priced lower and outside a predetermined near-price margin; and has the same primary scenario as said selected product.

19. A product summary generator for generating a naturally reading narrative product summary including assertions about a specific product selected by a user, said product summary generator comprising:

a processor coupled to a memory device configured to execute:

a product attribute module adapted to determine at least one attribute associated with said specific product;

an alternative product selection module adapted to select an alternative product based on said at least one attribute; and

a summary generation module adapted to generate a naturally reading narrative by combining said at least one attribute and said alternative product with one or more retrieved assertion models so that said narrative includes a recommendation of said alternative product.

20. A method implemented by one or more computing devices for generating a naturally reading narrative product summary including assertions about a specific product selected by a user, said method comprising:

receiving, by at least one of the one or more computing devices, the selection of a first product associated with a plurality of attributes;

identifying, by at least one of the one or more computing devices, at least one attribute of the plurality of attributes for the first product;

selecting, by at least one of the one or more computing devices, a second product based on the at least one attribute; and

generating, by at least one of the one or more computing devices, the naturally reading narrative product summary including assertions about the first product and a recommendation of the second product.