🧑‍💻Mathematical model for AI based Tech Interview Prep system

We have conceptualized a mathematical model that captures the unique interactions and functionalities of the system. This model would aim to optimize the interview preparation process for users and provide a reliable hiring tool for B2B businesses. Here's a simplified representation of our model:

Let's define the following variables and functions:

• $U$: Set of all users (professionals) in the system.

• $B$: Set of all B2B businesses using the system for hiring.

• $Q$: Set of all questions available for interview preparation.

• $S$: Set of skills relevant to the technical interviews.

• $P(u, s)$: Proficiency function for user $u$ in skill $s$, which determines the user's skill level.

• $A(u, q)$: Assessment function for user $u$ answering question $q$, which outputs a score based on the user's response.

• $M(u)$: Matching function for user $u$ that aligns their skills with the job requirements of B2B businesses.

• $V(c)$: Verification function for credential $c$ on the blockchain, ensuring the credential's validity.

• $L$: Learning function that uses the MoE and LLM to generate personalized interview questions and materials.

• $T$: Transaction function that records user interactions and progress on the blockchain ledger.

The mathematical model for the HydraNode AI-Based Technical Interview Preparation System can be represented by the function $InterviewPrep$, which aims to maximize the proficiency of users and the matching accuracy for B2B businesses:

$$InterviewPrep(U, B, Q, S) = \max \left( \sum_{u \in U} \sum_{s \in S} P(u, s) + \sum_{u \in U} \sum_{q \in Q} A(u, q) + \sum_{u \in U} \sum_{b \in B} M(u) \right)$$

Subject to:

• $P(u, s)$ is improved by $L(u, S)$ through personalized learning.

• $A(u, q)$ is accurate and reflects the user's true capabilities.

• $M(u)$ correctly aligns user skills with B2B business requirements.

• $V(c)$ for all credentials $c$ claimed by users is true.

• $T(u)$ for all transactions by user $u$ is securely recorded on the blockchain.

This function aims to maximize the sum of user proficiency across all skills, the assessment scores for all interview questions, and the accuracy of matching users with B2B business job requirements. The constraints ensure that the learning materials are personalized, the assessments are accurate, the skill matching is precise, the credentials are verified, and all transactions are securely recorded.

By solving this optimization problem, HydraNode can ensure that its AI-Based Technical Interview Preparation System is effective for individual professionals' growth and equally valuable for B2B businesses in making informed hiring decisions.