Devansh’s AI Product Finder: How Building a Recommendation System Helps with College Admissions

Recommendation systems are everywhere. From online shopping to streaming platforms, AI-driven product discovery has become so normalized that many people forget how complex these systems actually are. For students, this creates a trap. Product recommendation projects often look impressive on the surface, but many lack depth once examined closely.

Admissions officers know this. They don’t evaluate recommendation systems by how many products are suggested. They evaluate them by how the student frames the problem, manages trade-offs, and reasons about user behavior.

Devansh Malhotra’s AI Product Finder stands out because it treats recommendation not as a convenience feature, but as a decision-making system. The project reveals how AI can guide users through overwhelming choice while respecting constraints like relevance, personalization, and trust.

Why Product Discovery Is a Real AI Problem

Finding the “right” product is rarely about matching keywords. It involves:

• Understanding user intent, which is often incomplete or ambiguous

• Balancing personalization with exploration

• Avoiding bias toward popular or sponsored items

• Making recommendations that are explainable, not mysterious
  

From an AI standpoint, product discovery sits at the intersection of machine learning, user modeling, and systems design.

Devansh’s project began by acknowledging a key truth:

More choices do not improve decision-making. Better filtering does.

That insight shaped how the system was designed.

Case Study: Growth Through Practical Challenges

At the start, the project was primarily technical. Over time, it became more product-oriented. Devansh learned that:

• Good AI systems need clear problem definitions

• User experience shapes technical decisions

• Feedback loops improve both models and thinking
  

This evolution is exactly what admissions officers look for: evidence that the student grows when exposed to real constraints.

System Design: Balancing Automation and User Control

The AI Product Finder was built as a recommendation system that responds to user inputs and constraints. The technical stack focused on:

• Feature extraction from product data

• Matching user preferences to product attributes

• Ranking and filtering logic

• Continuous refinement based on feedback
  

Rather than presenting a single “best” product, the system emphasizes ranked suggestions, allowing users to retain agency.

This design choice reflects real-world practice. Mature AI systems assist decisions; they do not replace them.

Technical Learning Through Practical Constraints

What made this project educational was not complexity for its own sake, but constraint-driven learning.

Devansh had to reason through questions such as:

• How specific should user inputs be?

• What happens when preferences conflict?

• How do we avoid overfitting recommendations to limited data?
  

These questions force students to confront limitations, something admissions officers value highly.

The learning came from iteration. Early versions of the system were either too generic or too narrow. Through testing and mentorship feedback, the model evolved toward balanced recommendations.

Why Mentorship Matters in Projects Like This

Recommendation systems appear simple until students attempt to build them. Without guidance, many get stuck optimizing superficial metrics or adding unnecessary complexity.

Devansh’s reflection highlights the importance of a structured environment:

• Hands-on projects anchored learning in reality

• Mentorship helped refine both technical and design decisions

• Practical challenges exposed gaps that theory alone would not reveal
  

This mirrors how real engineers learn. They don’t grow by watching tutorials. They grow by building, failing, and revising with feedback.

Comparing Common Recommendation Projects to This One

Typical Student Project

• Static dataset

• One-time recommendation output

• Minimal discussion of user intent
  

AI Product Finder

• Preference-aware system design

• Emphasis on relevance and ranking

• Consideration of user experience and trust

The second approach demonstrates readiness for higher-level academic and professional work.

Admissions Perspective: Why This Project Works

From an admissions lens, the AI Product Finder signals several strengths:

• Applied reasoning: The student understands why the system exists

• Technical growth: Learning through hands-on challenges

• Professional awareness: Treating AI as a product, not just a model

• Reflection: Recognizing how mentorship accelerated learning
  

These qualities matter more than raw technical difficulty.

What This Teaches About AI Education

Projects like this highlight a broader truth. Learning AI in isolation rarely builds judgment. Learning AI through applied, mentored projects does.

Students who combine:

• Hands-on implementation

• Expert guidance

• Iterative problem-solving

develop skills that transfer well to college research, internships, and startups.

Frequently Asked Questions

Are recommendation systems too common for college applications?

They are common. Well-reasoned ones are not.

Does this project show depth beyond coding?

Yes. It demonstrates product thinking, user modeling, and decision support.

How important is mentorship in projects like this?

Very. Mentorship accelerates learning by preventing shallow solutions.

Can this project support majors beyond computer science?

Absolutely. It connects to business, data science, and human-computer interaction.

Final Perspective and Where to Learn More

Recommendation systems shape how people discover information, products, and opportunities. Building one responsibly requires more than technical skill. It requires understanding users, trade-offs, and consequences.

Devansh Malhotra’s AI Product Finder reflects that understanding. It shows how a student can move beyond surface-level AI and engage with real decision-making systems.

Programs like the AI & ML initiatives at BetterMind Labs are designed to create exactly this kind of growth. By combining hands-on projects with close mentorship, students gain both technical confidence and professional perspective.

To explore similar student projects or learn how structured mentorship supports meaningful AI learning, visit bettermindlabs.org.

Want to get started? Checkout Top Resources to Learn AI & ML in 2025 for High School Students