METHOD AND SYSTEM OF PERFORMING GAP ANALYSIS

1 . A computer implemented method for evaluating a performance gap of a proposed personalization solution versus a default solution without need for system integration of a client device, comprising the steps of:

utilizing, by a computer device, historical data, said historical data including a sample of engagement and transactions of a specific user, and catalog feed;

simulating, by the computer device, user actions in two environments, said environments being said proposed solution and said default solution;

comparing, by the computer device, product exposure data between the two environments; and

generating, by the computer device, a report analyzing a number of sessions with transactions and/or engagement in each environment.

2 . The method of claim 1 , wherein said evaluation is an extraction of performance metrics at an attribute level.

3 . The method of claim 2 , wherein said performance metrics measures whether a given recommendation system is aligning with user preferences.

4 . The method of claim 1 , wherein said evaluation of the performance gap is computed across an entire user session.

5 . The method of claim 1 , wherein said evaluation of the performance gap is computed in a discrete time ordered way, and the said evaluation measures in time how quickly said system converges to said user's preference.

6 . A computer implemented method of measuring a recommendation system's performance in learning a user's preference, comprising the steps of:

measuring a personalization factor, wherein said personalization factor is a measure of an alignment of the recommendation system with users preferences;

computing information entropy defined as ‘S” for the personalization factor, to measure how well the recommendation system has learned a user's preferences, wherein a lower value for entropy indicates a higher degree of certainty in an assessment of the ability of the recommendation system to learn or not learn user preferences; wherein S is computed as,

S=−Σ k ( p k log p k )

where p k is a probability distribution over discrete outcomes k,

S=−I log I −(1− I )log(1− I ),

wherein, when I>½, the system is learning the user's preferences with certainty S,

when I<½, the system is not learning the user's preferences with certainty S,

when, I=½, then S=1, indicating maximum entropy and uncertainty for the recommendation system.

7 . The method of claim 6 , wherein said information entropy is applied as a meta metric to frame the recommendation system in context of feedback loops, and the recommendation system is modeled as a gradation of learning algorithms.

8 . The method of claim 6 , wherein said personalization factor is used as a metric to compare performance of different recommendation engines.

9 . The method of claim 6 , wherein said performance of the recommendation system is tracked in real-time, and quantitative thresholds modulate output of said system's recommendation engine.

10 . The method of claim 6 , wherein based on the measure of the personalization factor and its associated information entropy for a fixed output from a recommendation engine, a strong performance metric amplifies a recommendation system's preference for a given recommendation engine, while a weak performance allows the recommendation system to change in real-time to another recommendation engine and track the subsequent output of said another recommendation engine.

11 . A computer implemented method of selecting between a plurality of personalization solutions offered to a specific user of a client device, comprising:

providing, by a computer device, a plurality of recommendation engines, wherein each recommendation engine provides a personalized solution;

measuring, by the computer device, a performance gap of each of said personalization solution versus a default solution without need for system integration, comprising the steps of:

utilizing historical data, said historical data including a sample of engagement and transactions of a specific user, and catalog feed;

simulating user actions in two environments, said environments being said personalized solution and said default solution;

comparing product exposure data between the two environments; and

computing a personalization factor for said personalized solution and default solution; and

selecting, by the computer device, one of said recommendation engines with the highest of said personalization factors.

12 . A method of adaptive learning of a recommendation engine for a client device, comprising:

measuring, by a computer device, a performance gap of each of a personalization solution versus a default solution using a discrete time approach, without need for system integration, comprising the steps of:

utilizing historical data, said historical data including a sample of engagement and transactions of a specific user, and catalog feed;

simulating user actions in two environments, said environments being said proposed solution and said default solution;

comparing product exposure data between the two environments; and

computing a personalization factor for said proposed solution and default solution; and

tracking, by the computer device, performance of the recommendation engine in real-time, and setting quantitative thresholds to modulate the output of said recommendation engine.

13 . A computer implemented system, comprising:

a processing subsystem;

and a memory subsystem storing instructions that cause the processing subsystem to perform operations comprising:

selecting a plurality of personalization solutions offered to a specific user of a client device, comprising the steps of:

providing a plurality of recommendation engines, wherein each recommendation engine provides a personalized solution;

measuring a performance gap of each of said personalization solution versus a default solution without need for system integration, comprising the steps of:

utilizing historical data, said historical data including a sample of engagement and transactions of a specific user, and catalog feed;

simulating user actions in two environments, said environments being said proposed solution and said default solution;

comparing product exposure data between the two environments; and

computing a personalization factor for said proposed solution and default solution; and

selecting one of said recommendation engines with the highest of said personalization factors.