Text Wrapping + Graph Rec: Ace Two Types of Questions

Background

In top tech company interviews (Google, Amazon, Netflix), interviewers love to test cognitive agility. One moment you are handling low-level string logic, the next you are switching to high-level recommendation algorithms. This "cold-hot switch" can easily cause candidates to freeze.

Recently, a student encountered this typical "combo" in an interview: a greedy text processing problem followed by a graph-based recommendation system problem. With oavoservice's "Real-time Logic Breakdown + Algorithm Model Mapping + Edge Case Warning" assistance, he not only wrote beautiful code but also demonstrated high professionalism in his explanation.

Question 1: Word Wrapping

📜 Essence of the Problem

Given a string containing irregular spaces and a maximum width. You are required to wrap lines by word (words cannot be split). Each line should pack as many words as possible (greedy strategy). Return the total number of lines required.

Note: If a single word length exceeds width, it counts as a separate line.

The Trap

Seemingly simple string processing, but hides details:

1. Dirty Input: " hello world " -> Preprocessing required.
2. Greedy Logic: current_len + 1 (space) + word_len <= width.
3. Edge Case: Single word longer than width.

oavoservice Real-time Guidance

Step 1: Clean Data

"First, tell the interviewer you'll split the string by space to get a clean word list. This simplifies the problem."

Step 2: Simulation Loop

"Use a current_line_length. Iterate through words. If adding the next word fits, add it; otherwise, reset current_line_length and increment lines."

Step 3: Edge Case

"Don't forget the case where len(word) > width. Clarify with the interviewer: should it be split or stay on its own line?"

Result: The candidate wrote a clean, bug-free Greedy implementation in 10 minutes.

Question 2: "Close Friend Recommendation" (Graph BFS)

📜 Essence of the Problem

Given a social graph, recommend "Friends of Friends" who are not yet directly connected to the user, ranked by "Number of Mutual Friends".

oavoservice's Model Mapping

We immediately prompted: "This is a standard Graph BFS (Level 2 Neighbors) + Frequency Count problem."

Steps:

1. Get Level 1 Neighbors: direct_friends.
2. Get Level 2 Neighbors: Iterate through direct_friends, find their friends.
3. Filter & Count:
   • Exclude user themselves.
   • Exclude direct_friends.
   • Use a Hash Map to count occurrences (Mutual Friends).
4. Sort: Top K by count.

Result: The candidate quickly mapped the abstract requirement to BFS + HashMap + Heap and completed the implementation and explanation in 15 minutes. The interviewer commented: "Very engineering-oriented mindset, not just LeetCoding."

🎯 Summary: oavoservice is Your "Navigation Tower"

In interviews with large spans:

• Clear Logic: Strip away distractions (like extra spaces) to hit the core logic.
• See the Essence: Instantly identify the mathematical model or algorithm paradigm behind the business problem.
• Cover Details: Real-time reminders for edge cases like "super long words" or "insufficient quantity".

Contact oavoservice to be your strong backing and secure your Offer.

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