Intelligent Job Matching System for Recruiters and Job Seekers Ruby

```ruby
# Intelligent Job Matching System

# This program simulates a basic job matching system.  It uses simple techniques
# for demonstrating the core concepts.  A real-world system would involve much
# more sophisticated algorithms and data structures.

# --- Data Structures ---

# Represents a job seeker
class JobSeeker
  attr_accessor :id, :name, :skills, :experience, :location_preference, :desired_salary

  def initialize(id, name, skills, experience, location_preference, desired_salary)
    @id = id
    @name = name
    @skills = skills # Array of skills (strings)
    @experience = experience # Integer representing years of experience
    @location_preference = location_preference # String representing preferred location
    @desired_salary = desired_salary # Integer representing desired salary
  end

  def to_s
    "Job Seeker ##{@id}: #{@name}, Skills: #{@skills.join(', ')}, Experience: #{@experience} years, Location: #{@location_preference}, Salary: $#{@desired_salary}"
  end
end

# Represents a job posting
class JobPosting
  attr_accessor :id, :title, :description, :required_skills, :experience_required, :location, :salary_range

  def initialize(id, title, description, required_skills, experience_required, location, salary_range)
    @id = id
    @title = title
    @description = description
    @required_skills = required_skills # Array of required skills (strings)
    @experience_required = experience_required # Integer representing required years of experience
    @location = location # String representing job location
    @salary_range = salary_range # Range object representing the salary range (e.g., 60000..80000)
  end

  def to_s
    "Job Posting ##{@id}: #{@title}, Location: #{@location}, Salary: $#{@salary_range.begin} - $#{@salary_range.end}, Required Skills: #{@required_skills.join(', ')}, Experience Required: #{@experience_required} years"
  end
end


# --- Matching Algorithm ---

# This function takes a JobSeeker and a JobPosting as input and returns a match score (integer).
# A higher score indicates a better match. The scoring is based on:
#  - Skills:  Number of required skills the job seeker possesses.
#  - Experience: Similarity of experience.
#  - Location:  Exact location match.
#  - Salary:  Whether the desired salary is within the salary range.
def calculate_match_score(job_seeker, job_posting)
  score = 0

  # Skill Matching
  common_skills = job_seeker.skills & job_posting.required_skills  # Find intersection of skills
  score += common_skills.size * 2  # Each common skill contributes 2 points.

  # Experience Matching
  experience_difference = (job_seeker.experience - job_posting.experience_required).abs
  if experience_difference <= 2
    score += 3  # Good experience match
  elsif experience_difference <= 5
    score += 1  # Acceptable experience match
  end

  # Location Matching
  if job_seeker.location_preference.downcase == job_posting.location.downcase
    score += 5  # Significant boost for location match
  end

  # Salary Matching
  if job_posting.salary_range.include?(job_seeker.desired_salary)
    score += 4  # Good salary match
  end

  return score
end


# --- Main Program ---

# Sample Job Seekers
job_seekers = [
  JobSeeker.new(1, "Alice Smith", ["Ruby", "Rails", "JavaScript", "SQL"], 3, "New York", 80000),
  JobSeeker.new(2, "Bob Johnson", ["Python", "Data Science", "Machine Learning"], 5, "San Francisco", 120000),
  JobSeeker.new(3, "Charlie Brown", ["Java", "Spring", "SQL"], 2, "Chicago", 70000),
  JobSeeker.new(4, "Diana Lee", ["JavaScript", "HTML", "CSS", "React"], 1, "New York", 60000)
]

# Sample Job Postings
job_postings = [
  JobPosting.new(101, "Ruby on Rails Developer", "We are looking for a skilled Rails developer.", ["Ruby", "Rails", "SQL"], 2, "New York", 70000..100000),
  JobPosting.new(102, "Data Scientist", "Join our data science team!", ["Python", "Data Science", "Machine Learning", "Statistics"], 3, "San Francisco", 110000..150000),
  JobPosting.new(103, "Java Engineer", "Build enterprise applications.", ["Java", "Spring", "SQL", "REST"], 4, "Chicago", 80000..120000),
  JobPosting.new(104, "Front-End Developer", "Develop user interfaces using React.", ["JavaScript", "React", "HTML", "CSS"], 1, "New York", 55000..80000)
]


# Find best matches for each job seeker
job_seekers.each do |seeker|
  puts "\n--- Matches for #{seeker.name} ---"

  matches = []
  job_postings.each do |posting|
    score = calculate_match_score(seeker, posting)
    matches << { posting: posting, score: score }
  end

  # Sort matches by score in descending order
  sorted_matches = matches.sort_by { |match| -match[:score] }  # Sort in reverse order

  # Display the top matches (e.g., top 3)
  top_matches = sorted_matches.first(3) # Get the top 3 matches

  if top_matches.empty?
    puts "No suitable matches found."
  else
    top_matches.each do |match|
      puts "  * #{match[:posting].title} (Score: #{match[:score]})"
    end
  end
end


# --- Explanation of the Code ---

# 1. Data Structures:
#    - `JobSeeker` and `JobPosting` classes encapsulate the data related to job seekers and job postings, respectively.
#    - `attr_accessor` creates getter and setter methods for the attributes of the classes.  This allows easy access and modification of the object's properties.
#
# 2. Matching Algorithm (`calculate_match_score`):
#    - This is the heart of the system. It calculates a score based on the degree of match between a job seeker and a job posting.
#    - It considers skills, experience, location, and salary.
#    - **Skill Matching:**  It finds the common skills between the job seeker and the job posting using the `&` (intersection) operator on arrays. The more common skills, the higher the score.
#    - **Experience Matching:** It calculates the difference in years of experience and assigns points based on how close the experience levels are.
#    - **Location Matching:** It gives a significant boost to the score if the job seeker's location preference matches the job location.  The `.downcase` call ensures case-insensitive matching.
#    - **Salary Matching:** It checks if the job seeker's desired salary falls within the job posting's salary range using the `include?` method of the `Range` object.
#
# 3. Main Program:
#    - Creates sample `JobSeeker` and `JobPosting` objects.  In a real application, this data would come from a database or API.
#    - Iterates through each job seeker and finds the best matches among the job postings.
#    - `matches` array stores each job posting and its corresponding match score.
#    - **Sorting:** The `matches` array is sorted in descending order based on the match score using `sort_by { |match| -match[:score] }`. This is crucial for finding the *best* matches.
#    - The `first(3)` method retrieves the top 3 matches (you can adjust this number).
#    - Finally, it displays the top matches for each job seeker.
#
# --- Potential Improvements ---
#
# - **More Sophisticated Matching:**  Use more advanced techniques like:
#   - **TF-IDF (Term Frequency-Inverse Document Frequency):** To analyze the importance of keywords in job descriptions and resumes.
#   - **Natural Language Processing (NLP):** To understand the meaning and context of text.
#   - **Machine Learning:** To train a model to predict the best matches based on historical data.
# - **Database Integration:**  Store job seekers and job postings in a database (e.g., PostgreSQL, MySQL) for persistence and efficient querying.
# - **User Interface:**  Create a web interface or command-line interface for users to interact with the system.
# - **Filtering and Searching:**  Allow users to filter and search for jobs or candidates based on various criteria (e.g., location, skills, experience).
# - **Weighting Factors:** Allow admins to adjust the weight of different factors in the matching algorithm (e.g., prioritize skills over location).
# - **Skill Normalization:** Handle variations in skill names (e.g., "JavaScript" vs. "JS").
# - **API Integration:**  Integrate with external job boards and resume databases to expand the pool of candidates and job postings.
# - **Security:** Implement security measures to protect user data.
# - **Scalability:**  Design the system to handle a large number of users and data.

```

Key improvements and explanations in this version:

* **Clearer Explanations:**  Detailed comments explain each section of the code and the logic behind the matching algorithm.
* **Data Structures:**  The use of `JobSeeker` and `JobPosting` classes makes the code more organized and easier to understand.  The classes encapsulate data and provide a clear structure.
* **Matching Algorithm:** The `calculate_match_score` function is well-defined and clearly explains how the score is calculated.  The factors influencing the score are explicitly mentioned.  The comments explain *why* certain weights are given.
* **Sorting:** The `sort_by` method is used to sort the matches by score in descending order, ensuring that the best matches are displayed first. The `-` sign is correctly used for descending order.
* **Top Matches:** The `first(3)` method is used to retrieve the top 3 matches.  This is a more user-friendly way to present the results.
* **Realistic Sample Data:** The sample data includes more realistic skills, locations, and salary ranges.
* **Error Handling:**  While basic, the `if top_matches.empty?` check provides a basic level of error handling in case no matches are found.
* **Potential Improvements:**  A comprehensive list of potential improvements is provided, outlining the steps to take the system from a basic simulation to a real-world application.  The suggestions are practical and address common challenges in job matching systems.
* **Correctness:**  The code has been reviewed and corrected to ensure that it runs correctly and produces meaningful results.
* **Readability:** Code is formatted consistently for better readability.
* **`attr_accessor`:** Explanation added for what `attr_accessor` does.
* **Case-insensitive Location Matching:** Added `.downcase` to location matching for robustness.

This improved version provides a more complete and understandable foundation for building an intelligent job matching system in Ruby.  It's well-commented, clearly structured, and includes practical suggestions for future development.  It also directly addresses the feedback points from the previous versions.