CLIAMS:We claim:
1. A computer-implemented method for providing product recommendations, the method comprising:
determining, by a processor, personality traits of a sender and a recipient by applying a five-factor model to a plurality of datasets obtained from user data;
associating, by the processor, a personality-product score with each of a plurality of products based on the personality traits;
performing, by the processor, a need analysis on the user data to determine desired products from amongst the plurality of products;
determining, by the processor, a multidimensional collaborative matrix by aggregating the personality traits of the sender and the recipient, the personality-product score, the desired products, and product psychographic portfolio, wherein the product psychographic portfolio comprises elasticity of affinity toward each of the plurality of products with respect to the personality traits of the sender and the recipient;
determining, by the processor, an affinity score for at least one of the sender and the recipient towards each of the plurality of products based on the multidimensional collaborative matrix; and
recommending, by the processor, at least one product from amongst the plurality of products to the sender based on the affinity score.

2. The method of claim 1 further comprises:
receiving a feedback, from the sender, on the at least one product recommended;
determining a revised affinity score for the at least one product based on the feedback; and
recommending new products from the plurality of products based on the revised affinity score.

3. The method of claim 1, wherein determining the personality traits further comprises:
receiving user data, comprising information about the sender and the recipient, from at least one data source; and
segmenting the user data, based on segmentation rules, to obtain the plurality of datasets.

4. The method of claim 3, wherein receiving the user data further comprises:
initializing configuration settings;
receiving the user data from the at least one data source;
preprocessing the user data based on the configuration settings;
correlating data attributes obtained from the at least one data source based on the preprocessing; and
analyzing correlated data attributes to validate the user data.

5. The method of claim 1, wherein the personality-product score indicates correlation of the personality traits with lifecycle stages of the plurality of products.

6. The method of claim 1, wherein the product psychographic portfolio is determined based on a product list and big-five elasticity coefficients, wherein the product list comprises products with high priority in a management information system (MIS), and wherein the big-five elasticity coefficients indicate variation in affinity towards a product with respect to variation in each of the personality traits.

7. The method of claim 1 further comprises monitoring responses received from the sender and the recipient post-recommendation to improve further recommendations.

8. A recommendation system for providing product recommendations, the recommendation system comprising:
a processor operatively coupled to a memory device, wherein the processor is configured to execute instructions stored in the memory device to perform operations comprising:
determining personality traits of a sender and a recipient by applying a five-factor model to a plurality of datasets obtained from user data;
associating a personality-product score with each of a plurality of products based on the personality traits;
performing a need analysis on the user data to determine desired products from amongst the plurality of products;
determining a multidimensional collaborative matrix by aggregating the personality traits of the sender and the recipient, the personality-product score, the desired products, and product psychographic portfolio, wherein the product psychographic portfolio comprises elasticity of affinity toward each of the plurality of products with respect to the personality traits of the sender and the recipient;
determining an affinity score for at least one of the sender and the recipient towards each of the plurality of products based on the multidimensional collaborative matrix; and
recommending at least one product from amongst the plurality of products to the sender based on the affinity score.

9. The system of claim 8, wherein the operations further comprise:
receiving a feedback, from the sender, on the at least one product recommended;
determining a revised affinity score for the at least one product based on the feedback; and
recommending new products from the plurality of products based on the revised affinity score.

10. The system of claim 8, wherein the operations of determining the personality traits further comprises:
receiving user data, comprising information about the sender and the recipient, from at least one data source; and
segmenting the user data, based on segmentation rules, to obtain the plurality of datasets.

11. The system of claim 10, wherein receiving the user data further comprises:
initializing configuration settings;
receiving the user data from the at least one data source;
preprocessing the user data based on the configuration settings;
correlating data attributes obtained from the at least one data source based on the preprocessing; and
analyzing correlated data attributes to validate the user data.

12. The system of claim 8, wherein the personality-product score indicates correlation of the personality traits with lifecycle stages of the plurality of products.

13. The system of claim 8, wherein the product psychographic portfolio is determined based on a product list and big-five elasticity coefficients, wherein the product list comprises products with high priority in a management information system (MIS), and wherein the big-five elasticity coefficients indicate variation in affinity towards a product with respect to variation in each of the personality traits.

14. The system of claim 8, wherein the operations further comprise monitoring responses received from the sender and the recipient post-recommendation to improve further recommendations.

15. A non-transitory computer-readable medium storing instructions for providing product recommendations that, when executed by a processor, cause the processor to perform operations comprising:
determining personality traits of a sender and a recipient by applying a five-factor model to a plurality of datasets obtained from user data;
associating a personality-product score with each of a plurality of products based on the personality traits;
performing a need analysis on the user data to determine desired products from amongst the plurality of products;
determining a multidimensional collaborative matrix by aggregating the personality traits of the sender and the recipient, the personality-product score, the desired products, and product psychographic portfolio, wherein the product psychographic portfolio comprises elasticity of affinity toward each of the plurality of products with respect to the personality traits of the sender and the recipient;
determining an affinity score for at least one of the sender and the recipient towards each of the plurality of products based on the multidimensional collaborative matrix; and
recommending at least one product from amongst the plurality of products to the sender based on the affinity score.

Dated this 24th day of September, 2014
SHWETHA A CHIMALGI
OF K & S PARTNERS
AGENT FOR THE APPLICANTS
,TagSPECI:FIELD OF THE INVENTION
The present subject matter relates to recommender systems, and, particularly but not exclusively, to systems and methods for providing product recommendations.