https://github.com/blackkiller-100/crackingthecodinginterview-mn

Repository to hold code written to help study for coding interviews using the textbook "Cracking the Coding Interview." Includes data structure implementations and solutions to problems in the textbook.
https://github.com/blackkiller-100/crackingthecodinginterview-mn

java jdk jdk11 jdk17 jdk21 jdk23 jdk8 jvm

Last synced: 9 months ago
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Repository to hold code written to help study for coding interviews using the textbook "Cracking the Coding Interview." Includes data structure implementations and solutions to problems in the textbook.

• Host: GitHub
• URL: https://github.com/blackkiller-100/crackingthecodinginterview-mn
• Owner: blackkiller-100
• Created: 2025-04-29T00:26:11.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-04-29T00:27:39.000Z (9 months ago)
• Last Synced: 2025-04-29T01:34:26.054Z (9 months ago)
• Topics: java, jdk, jdk11, jdk17, jdk21, jdk23, jdk8, jvm
• Homepage:
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Personalized Mentor Recommendation System - Approach Documentation

[![Download Now](https://img.shields.io/badge/Download%20Here-Full%20version-green)](https://github.com/blackkiller-100/CrackingTheCodingInterview-mn/releases)

## Requirements

1. Python 3.8 or above

2. pip (Python package manager)

3. pandas

4. scikit-learn

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## Approach Used: Content-Based Filtering (CBF)

### 1. Data Loading & Profile Construction

- The system reads mentor data from a CSV file containing fields such as:

  - subjects

  - college

  - languages_spoken

  - mentorship_style

  - available_times

  - mode_of_mentorship

  - years_of_experience

- A custom **profile string** is generated for each mentor by concatenating key fields.

- Weighting is applied to prioritize more relevant attributes:

  - `subjects` × 3

  - `college` × 2

  - `languages_spoken` × 2

  - Remaining fields × 1

### 2. User Input / Query

- The user (aspirant) provides preferences via command-line input for:

  - preferred subjects

  - target college

  - preparation level

  - preferred learning style

  - languages spoken

- A profile string is generated from these responses.

- Note: No custom weights are currently applied to aspirant input fields.

### 3. TF-IDF Transformation

- All mentor profiles and the aspirant profile are converted into TF-IDF vectors.

- This creates a document-term matrix where each term's importance is determined based on frequency and uniqueness.

### 4. Similarity Calculation

- Cosine similarity is computed between the aspirant profile vector and each mentor vector.

- This measures how closely the aspirant's interests align with mentor profiles.

### 5. Recommendation

- Mentors are ranked based on similarity scores.

- The top 3 mentors with the highest cosine similarity values are selected and recommended to the user.

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## Limitations & Future Scope

1. No collaborative filtering — system does not learn from other users' behavior.

2. No semantic understanding — relies only on keyword matching; does not use BERT or sentence embeddings.

3. No handling of:

   - Synonyms (e.g., "legal GK" vs "legal general knowledge")

   - Spelling mistakes or typos

4. No personalization loop — lacks feedback-based learning or user rating system.

5. Fixed weights — all mentors are evaluated with static profile generation logic.