XMARAX2A User Guide Introduction XMARAX2A is a career-driven job search system optimized for ATS compatibility, career path tracking, and industry transferability. This guide provides function calls, voice commands, pathways, and best practices to use XMARAX2A efficiently. 1️⃣ System Initialization Voice Command: 🗣️ "Initialize XMARAX2A." Type Command: configurator = ConfiguratorCore("persona_database.json", "job_market_requirements.json") configurator.initialize_system() What It Does: ✅ Loads persona and job data from JSON files. ✅ Initializes career mapping and job search modules. ✅ Runs system checks and ensures rollback safety. 2️⃣ Career Mapping & Visualization Voice Command: 🗣️ "Show my career path." Type Command: configurator.career_mapper.plot_career_map() What It Does: ✅ Displays an interactive career path visualization using Plotly. ✅ Highlights current role and potential career progression paths. ✅ Allows users to see forward and backward paths for career planning. Troubleshooting Career Map Issues: 🔹 Ensure dependencies are installed: pip install plotly networkx 🔹 Verify persona database has defined career paths. 🔹 If an error occurs, reset with: configurator.rollback() 3️⃣ Optimized Job Search (ATS-Enhanced & Career-Based Ranking) Voice Command: 🗣️ "Find my best job matches." Type Command: ranked_jobs = configurator.job_search.prioritize_jobs() configurator.job_search.display_ranked_jobs() What It Does: ✅ Prioritizes job listings based on ATS keyword matching, career path relevance, and industry adaptability. ✅ Uses Graph Advancement Score (GAS) to determine best career-aligned roles. ✅ Ensures job descriptions match persona skills and growth trajectory. 🔹 Best Used For: Getting highly targeted, best-fit job recommendations based on your long-term career goals. 4️⃣ Standard Job Search (General Ranking Without Optimization) Voice Command: 🗣️ "Show me all jobs that match my resume." Type Command: ranked_jobs = configurator.job_search.ats_system.rank_jobs(configurator.persona_data) print("Standard Ranked Jobs:", ranked_jobs) What It Does: ✅ Ranks jobs based purely on ATS keyword matching (without factoring career trajectory). ✅ Uses TF-IDF scoring to find resume-to-job description matches. ✅ Ignores career progression logic—useful for broad, flexible job searches. 🔹 Best Used For: Finding any relevant job based on current resume and keyword matching. 5️⃣ Custom Job Search (User-Priority Adjustments) Voice Command: 🗣️ "Customize my job search preferences." Type Command: configurator.persona_data["user_preferences"]["weight"] = 1.0 # Adjust priority weight ranked_jobs = configurator.job_search.prioritize_jobs() configurator.job_search.display_ranked_jobs() What It Does: ✅ Allows custom weighting for job search preferences. ✅ Adjusts results to prioritize specific industries, job types, or locations. ✅ Gives user control over job ranking criteria. 🔹 Best Used For: Fine-tuning job search based on personal priorities. 6️⃣ Industry Transferability Check (Cross-Industry Job Matching) Voice Command: 🗣️ "How well do my skills transfer to [Industry Name]?" Type Command: score = configurator.ats_system.industry_transferability_score("Marketing") print("Industry Transferability Score:", score) What It Does: ✅ Checks how well your skills transfer to a different industry. ✅ Uses the industry_interaction_map for adaptability scoring. ✅ Helps you explore career shifts into related fields. 🔹 Best Used For: Checking career flexibility and transition opportunities. 7️⃣ Debugging & Rollback System Voice Command: 🗣️ "Reset system to last stable version." Type Command: configurator.rollback() What It Does: ✅ Resets system components to prevent crashes. ✅ Reverts to the last stable state in case of errors. ✅ Ensures a safe recovery process for system failures. 🔹 Best Used For: Fixing unexpected errors without corrupting data. 8️⃣ JSON Data Management (Manually Update Persona or Job Market Data) Voice Command: 🗣️ "Update my career path to [New Role]." Type Command: configurator.persona_data["profile"]["candidate_position"] = "AI Content Strategist" Update Job Listings: new_job = { "name": "AI Ethics Consultant", "description": "Looking for AI professionals with ethics expertise.", "industry": "AI", "required_skills": ["AI Governance", "Ethical AI"] } configurator.job_market_data["jobs"].append(new_job) What It Does: ✅ Allows manual updates to persona details, career paths, or job listings. ✅ Lets you add new industries, skills, or preferences dynamically. ✅ Expands system flexibility for long-term job market adaptation. 🔹 Best Used For: Keeping data fresh and relevant to evolving career goals. 🚀 Summary: How to Fully Operate XMARAX2A Basic Startup: 🗣️ "Initialize XMARAX2A." 1️⃣ Run configurator.initialize_system() to load data. 2️⃣ Run configurator.career_mapper.plot_career_map() to visualize career trajectory. 3️⃣ Run configurator.job_search.display_ranked_jobs() to see optimized job matches. Search Options: 🔹 Optimized Search → 🗣️ "Find my best job matches." 🔹 Standard Search → 🗣️ "Show me all jobs that match my resume." 🔹 Custom Search → 🗣️ "Customize my job search preferences." 🔥 With these commands, you can fully operate XMARAX2A efficiently! Let me know if you need refinements! 🚀
AI Job Market Report 9
{
"jobs": [
{
"name": "AI Content Strategist",
"description": "Looking for an AI expert with NLP skills and machine learning experience.",
"industry": "AI",
"required_skills": ["NLP", "Machine Learning"],
"salary_range": "$80,000 - $100,000"
},
{
"name": "Data Scientist",
"description": "Seeking a data professional with experience in ML, statistics, and Python.",
"industry": "Tech",
"required_skills": ["Python", "Statistics", "Machine Learning"],
"salary_range": "$90,000 - $120,000"
},
{
"name": "AI Research Manager",
"description": "Oversee AI projects, collaborate with researchers, and optimize AI strategies.",
"industry": "AI",
"required_skills": ["AI Research", "Machine Learning", "Leadership"],
"salary_range": "$120,000 - $150,000"
},
{
"name": "Technical Editor",
"description": "Edit and refine AI-generated content for accuracy and readability.",
"industry": "Marketing",
"required_skills": ["Editing", "Content Review", "AI Writing Evaluation"],
"salary_range": "$60,000 - $80,000"
}
]
}
--------------------------------------------------------------------------------
AI Career Path Mapping 8
{
"profile": {
"candidate_position": "AI Writing Evaluator",
"resume": "Experienced AI Writing Evaluator with skills in NLP, Machine Learning, and content analysis."
},
"skills": ["AI", "Machine Learning", "NLP", "Content Analysis"],
"career_paths": {
"AI Writing Evaluator": {
"FORWARD_PATH": ["AI Content Strategist"],
"BACKWARD_PATH": ["Content Moderator"]
},
"AI Content Strategist": {
"FORWARD_PATH": ["AI Research Manager"],
"BACKWARD_PATH": ["AI Writing Evaluator"]
},
"Content Moderator": {
"FORWARD_PATH": ["AI Writing Evaluator"],
"BACKWARD_PATH": ["Entry-Level Editor"]
},
"AI Research Manager": {
"FORWARD_PATH": ["Head of AI Research"],
"BACKWARD_PATH": ["AI Content Strategist"]
}
},
"industry_interaction_map": {
"AI": 1.0,
"Tech": 0.8,
"Marketing": 0.6
},
"user_preferences": {
"weight": 0.8,
"preferred_industries": ["AI", "Tech"],
"preferred_roles": ["AI Content Strategist", "AI Research Manager"]
}
}
XMARAX2A System Initialization and Job Search 7
from job_search import JobSearch
from career_mapping import CareerMapping
import json
class ConfiguratorCore:
def __init__(self, persona_file, job_market_file):
self.persona_data = self.load_json(persona_file)
self.job_market_data = self.load_json(job_market_file)
self.career_mapper = None
self.job_search = None
def load_json(self, file_path):
"""Loads JSON data safely with error handling."""
try:
with open(file_path, "r", encoding="utf-8") as file:
return json.load(file)
except (FileNotFoundError, json.JSONDecodeError) as e:
print(f"⚠️ Error loading {file_path}: {e}")
return {}
def initialize_system(self):
"""Ensures all system components load correctly with rollback safety."""
try:
self.career_mapper = CareerMapping(self.persona_data)
self.job_search = JobSearch(self.persona_data, self.job_market_data)
print("✅ XMARAX2A System Initialized Successfully!")
except Exception as e:
print(f"🚨 Initialization Failed: {e}")
self.rollback()
def rollback(self):
"""Handles failures and prevents system crashes."""
print("🔄 Rolling back to previous stable state...")
self.career_mapper = None
self.job_search = None
print("⚠️ System reset. Please resolve errors before retrying.")
def run(self):
"""Executes job search and career mapping visualization."""
if not self.career_mapper or not self.job_search:
print("❌ System components not loaded. Run initialize_system() first.")
return
self.career_mapper.plot_career_map()
self.job_search.display_ranked_jobs()
# Initialize the configurator with sample JSON files
configurator = ConfiguratorCore("persona_database.json", "job_market_requirements.json")
configurator.initialize_system()
configurator.run()
--------------------------------------------------------------------------------
Job Search Algorithm 6
from ats_algorithm import ATSAlgorithm
class JobSearch:
def __init__(self, persona_data, job_market_data):
self.persona_data = persona_data
self.job_market_data = job_market_data
self.ats_system = ATSAlgorithm(job_market_data, persona_data["resume"])
def prioritize_jobs(self):
"""Ranks job opportunities based on ATS optimization and career trajectory."""
ranked_jobs = self.ats_system.rank_jobs(self.persona_data)
return ranked_jobs
def display_ranked_jobs(self):
"""Prints the ranked job listings in order of best match."""
ranked_jobs = self.prioritize_jobs()
print("\\n🔹 Ranked Job Listings (Best Fit First) 🔹")
for rank, (job_name, score) in enumerate(ranked_jobs, 1):
print(f"{rank}. {job_name} - Score: {round(score, 2)}")
# Simulated Data for Testing
persona_data = {
"resume": "Experienced AI Writing Evaluator with skills in NLP, Machine Learning, and content analysis.",
"skills": ["AI", "Machine Learning", "NLP", "Content Analysis"],
"industry_interaction_map": {"AI": 1.0, "Tech": 0.8},
"user_preferences": {"weight": 0.8},
}
job_market_data = [
{
"name": "AI Content Strategist",
"description": "Looking for an AI expert with NLP skills",
"industry": "AI",
"required_skills": ["NLP", "Machine Learning"],
},
{
"name": "Data Scientist",
"description": "Seeking a data professional for ML and analytics",
"industry": "Tech",
"required_skills": ["Python", "Statistics"],
},
]
job_search = JobSearch(persona_data, job_market_data)
job_search.display_ranked_jobs()
Applicant Tracking System Optimization 5
from scipy.spatial.distance import cosine
from sklearn.feature_extraction.text import TfidfVectorizer
class ATSAlgorithm:
def __init__(self, job_data, user_resume):
self.job_data = job_data
self.user_resume = user_resume
def calculate_tfidf_scores(self, job_description):
"""Computes TF-IDF similarity between job description and user resume."""
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform([job_description, self.user_resume])
return 1 - cosine(tfidf_matrix[0].toarray()[0], tfidf_matrix[1].toarray()[0])
def calculate_persona_alignment(self, job_required_skills, persona_skills):
"""Measures how well a job aligns with the persona's skills."""
matching_skills = set(job_required_skills) & set(persona_skills)
return len(matching_skills) / len(job_required_skills) if job_required_skills else 0
def calculate_weighted_keyword_scores(self, job, persona_data):
"""Computes a weighted score for job ranking based on ATS compatibility and career trajectory."""
tfidf_score = self.calculate_tfidf_scores(job["description"])
persona_alignment = self.calculate_persona_alignment(job["required_skills"], persona_data["skills"])
industry_transferability = persona_data["industry_interaction_map"].get(job["industry"], 0.5)
return (
(tfidf_score * 0.4)
+ (persona_alignment * 0.2)
+ (industry_transferability * 0.1)
+ (persona_data["user_preferences"]["weight"] * 0.1)
)
def rank_jobs(self, persona_data):
"""Ranks job opportunities based on ATS optimization and persona alignment."""
ranked_jobs = []
for job in self.job_data:
score = self.calculate_weighted_keyword_scores(job, persona_data)
ranked_jobs.append((job["name"], score))
return sorted(ranked_jobs, key=lambda x: x[1], reverse=True)
# Simulated Data for Testing
persona_data = {
"resume": "Experienced AI Writing Evaluator with skills in NLP, Machine Learning, and content analysis.",
"skills": ["AI", "Machine Learning", "NLP", "Content Analysis"],
"industry_interaction_map": {"AI": 1.0, "Tech": 0.8},
"user_preferences": {"weight": 0.8},
}
job_data = [
{
"name": "AI Content Strategist",
"description": "Looking for an AI expert with NLP skills",
"industry": "AI",
"required_skills": ["NLP", "Machine Learning"],
},
{
"name": "Data Scientist",
"description": "Seeking a data professional for ML and analytics",
"industry": "Tech",
"required_skills": ["Python", "Statistics"],
},
]
ats_system = ATSAlgorithm(job_data, persona_data["resume"])
ranked_jobs = ats_system.rank_jobs(persona_data)
print("Ranked Jobs:", ranked_jobs)
--------------------------------------------------------------------------------
Interactive Career Path Visualization 4
import networkx as nx
import plotly.graph_objects as go
class CareerMapping:
def __init__(self, persona_data):
self.persona_data = persona_data
self.graph = nx.DiGraph()
self.build_career_graph()
def build_career_graph(self):
"""Constructs the directed graph for career pathing."""
for role, connections in self.persona_data["career_paths"].items():
for forward in connections.get("FORWARD_PATH", []):
self.graph.add_edge(role, forward)
for backward in connections.get("BACKWARD_PATH", []):
self.graph.add_edge(backward, role)
def calculate_graph_advancement_score(self, role_name):
"""Calculates a graph advancement score for a given role."""
if role_name not in self.graph.nodes():
print(f"⚠️ {role_name} does not exist in the career tree")
return 0
try:
shortest_path = nx.shortest_path(
self.graph,
source=self.persona_data["profile"]["candidate_position"],
target=role_name
)
return len(shortest_path) # A longer path means more career advancement required
except nx.NetworkXNoPath:
print(f"⚠️ No path found from {self.persona_data['profile']['candidate_position']} to {role_name}")
return 0
def plot_career_map(self):
"""Visualizes the career tree with Plotly for interactive viewing."""
if not self.graph:
print("⚠️ No graph data to plot, be sure that you called build_career_graph()")
return
pos = nx.spring_layout(self.graph, seed=42)
edge_x, edge_y = [], []
for edge in self.graph.edges():
if edge[0] not in pos or edge[1] not in pos:
continue
x0, y0 = pos[edge[0]]
x1, y1 = pos[edge[1]]
edge_x.extend([x0, x1, None])
edge_y.extend([y0, y1, None])
edge_trace = go.Scatter(
x=edge_x, y=edge_y,
line=dict(width=1, color='gray'),
mode='lines'
)
node_x, node_y, node_text, node_color = [], [], [], []
for node in self.graph.nodes():
if node not in pos:
continue
x, y = pos[node]
node_x.append(x)
node_y.append(y)
node_text.append(node)
node_color.append('green' if node == self.persona_data["profile"]["candidate_position"] else 'lightblue')
node_trace = go.Scatter(
x=node_x, y=node_y,
mode='markers+text',
marker=dict(size=15, color=node_color),
text=node_text,
textposition='top center'
)
fig = go.Figure(data=[edge_trace, node_trace])
fig.update_layout(title='Interactive Career Path Visualization', showlegend=False)
fig.show()
# Simulated Data for Testing
persona_data = {
"profile": {"candidate_position": "AI Writing Evaluator"},
"career_paths": {
"AI Writing Evaluator": {"FORWARD_PATH": ["AI Content Strategist"], "BACKWARD_PATH": ["Content Moderator"]},
"AI Content Strategist": {"FORWARD_PATH": ["AI Research Manager"], "BACKWARD_PATH": ["AI Writing Evaluator"]},
"Content Moderator": {"FORWARD_PATH": ["AI Writing Evaluator"], "BACKWARD_PATH": ["Entry-Level Editor"]},
}
}
career_map = CareerMapping(persona_data)
career_map.plot_career_map()
XMARAX2A: A Career Pathing Algorithm 3
import json
import networkx as nx
import math
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.spatial.distance import cosine
class XMARAX2A_Core:
def __init__(self, persona_data, job_data):
self.persona_data = persona_data
self.job_data = job_data
self.career_graph = nx.DiGraph()
self.build_career_graph()
def build_career_graph(self):
"""Constructs the directed graph for career pathing."""
for role, connections in self.persona_data["career_paths"].items():
for forward in connections.get("FORWARD_PATH", []):
self.career_graph.add_edge(role, forward)
for backward in connections.get("BACKWARD_PATH", []):
self.career_graph.add_edge(backward, role)
def calculate_tfidf_scores(self, job_description, user_resume):
"""Computes TF-IDF similarity between job description and resume."""
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform([job_description, user_resume])
return 1 - cosine(tfidf_matrix[0].toarray()[0], tfidf_matrix[1].toarray()[0])
def graph_advancement_score(self, target_role):
"""Calculates Graph Advancement Score based on career trajectory."""
if target_role not in self.career_graph:
return 0
try:
path_length = nx.shortest_path_length(
self.career_graph,
source=self.persona_data["profile"]["candidate_position"],
target=target_role,
)
return 1 / (1 + path_length) # Closer jobs get a higher score
except nx.NetworkXNoPath:
return 0
def industry_transferability_score(self, job_industry):
"""Retrieves industry adaptability score from interaction map."""
return self.persona_data["industry_interaction_map"].get(job_industry, 0.5) # Default 0.5 if undefined
def calculate_weighted_keyword_scores(self, job):
"""Computes the overall weighted score for job ranking."""
tfidf_score = self.calculate_tfidf_scores(job["description"], self.persona_data["resume"])
persona_alignment = self.calculate_persona_alignment(job)
career_progression = self.graph_advancement_score(job["name"])
industry_transferability = self.industry_transferability_score(job["industry"])
return (
(tfidf_score * 0.4)
+ (persona_alignment * 0.2)
+ (career_progression * 0.2)
+ (industry_transferability * 0.1)
+ (self.persona_data["user_preferences"]["weight"] * 0.1)
)
def calculate_persona_alignment(self, job):
"""Measures how well a job aligns with persona skills."""
matching_skills = set(job["required_skills"]) & set(self.persona_data["skills"])
return len(matching_skills) / len(job["required_skills"]) if job["required_skills"] else 0
def prioritize_jobs(self):
"""Ranks job opportunities based on computed scores."""
ranked_jobs = []
for job in self.job_data:
score = self.calculate_weighted_keyword_scores(job)
ranked_jobs.append((job["name"], score))
return sorted(ranked_jobs, key=lambda x: x[1], reverse=True)
# Simulated Data for Testing
persona_data = {
"resume": "Experienced AI Writing Evaluator with skills in NLP, Machine Learning, and content analysis.",
"skills": ["AI", "Machine Learning", "NLP", "Content Analysis"],
"profile": {"candidate_position": "AI Writing Evaluator"},
"career_paths": {
"AI Writing Evaluator": {"FORWARD_PATH": ["AI Content Strategist"], "BACKWARD_PATH": ["Content Moderator"]}
},
"industry_interaction_map": {"AI": 1.0, "Tech": 0.8},
"user_preferences": {"weight": 0.8},
}
job_data = [
{"name": "AI Content Strategist", "description": "Looking for an AI expert with NLP skills", "industry": "AI", "required_skills": ["NLP", "Machine Learning"]},
{"name": "Data Scientist", "description": "Seeking a data professional for ML and analytics", "industry": "Tech", "required_skills": ["Python", "Statistics"]},
]
xmarax2a_core = XMARAX2A_Core(persona_data, job_data)
ranked_jobs = xmarax2a_core.prioritize_jobs()
print("Ranked Jobs:", ranked_jobs)
--------------------------------------------------------------------------------
XMARAX2A Core Logic Integration Guide 2
### **Configurator Instructions for XMARAX2A Integration**
#### **Objective**:
Integrate the **XMARAX2A Core Logic** into the system while ensuring:
✅ Full **ATS Hybrid Search Algorithm compatibility**
✅ **Graph-Based Career Mapping stability**
✅ **Industry Transferability & Persona Alignment optimizations**
✅ **User Preference Customization & Job Ranking Enhancements**
✅ **Rollback Safety in Case of Failure**
---
### **1️⃣ Core Logic Setup (Main System Integration)**
**File: `xmarax2a_core.py`**
```python
import json
import networkx as nx
import math
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.spatial.distance import cosine
class XMARAX2A_Core:
def __init__(self, persona_data, job_data):
self.persona_data = persona_data
self.job_data = job_data
self.career_graph = nx.DiGraph()
self.build_career_graph()
def build_career_graph(self):
"""Constructs the directed graph for career pathing."""
for role, connections in self.persona_data["career_paths"].items():
for forward in connections.get("FORWARD_PATH", []):
self.career_graph.add_edge(role, forward)
for backward in connections.get("BACKWARD_PATH", []):
self.career_graph.add_edge(backward, role)
File: career_mapping.py
import plotly.graph_objects as go
def plot_career_map(graph, candidate_role):
"""Visualizes the career tree with Plotly for interactive viewing."""
if not graph:
print("⚠️ No graph data to plot, be sure that you called the build_career_graph function")
return
pos = nx.spring_layout(graph, seed=42)
edge_x, edge_y = [], []
for edge in graph.edges():
if edge[0] not in pos or edge[1] not in pos:
continue
x0, y0 = pos[edge[0]]
x1, y1 = pos[edge[1]]
edge_x.extend([x0, x1, None])
edge_y.extend([y0, y1, None])
edge_trace = go.Scatter(x=edge_x, y=edge_y, line=dict(width=1, color='gray'), mode='lines')
node_x, node_y, node_text, node_color = [], [], [], []
for node in graph.nodes():
if node not in pos:
continue
x, y = pos[node]
node_x.append(x)
node_y.append(y)
node_text.append(node)
node_color.append('green' if node == candidate_role else 'lightblue')
node_trace = go.Scatter(x=node_x, y=node_y, mode='markers+text', marker=dict(size=15, color=node_color), text=node_text, textposition='top center')
fig = go.Figure(data=[edge_trace, node_trace])
fig.update_layout(title='Interactive Career Path Visualization', showlegend=False)
fig.show()
File: ats_algorithm.py
from scipy.spatial.distance import cosine
from sklearn.feature_extraction.text import TfidfVectorizer
def calculate_tfidf_scores(job_description, user_resume):
"""Computes TF-IDF similarity between job description and resume."""
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform([job_description, user_resume])
return 1 - cosine(tfidf_matrix[0].toarray()[0], tfidf_matrix[1].toarray()[0])
File: job_search.py
def prioritize_jobs(persona_data, job_data):
"""Ranks job opportunities based on weighted scores."""
ranked_jobs = []
for job in job_data:
score = calculate_tfidf_scores(job["description"], persona_data["resume"])
ranked_jobs.append((job["name"], score))
return sorted(ranked_jobs, key=lambda x: x[1], reverse=True)
File: configurator_core.py
def initialize_xmarax2a(persona_data, job_market_data):
"""Ensures system loads all components safely with rollback options."""
try:
career_mapper = XMARAX2A_Core(persona_data, job_market_data)
return career_mapper
except Exception as e:
print(f"⚠️ Initialization Failed: {e}")
return None
🚀 This Configurator setup ensures: ✅ Modular Integration (separated files for structured processing). ✅ Stable Career Mapping (Plotly-based visualization & Graph Fixes). ✅ ATS Optimization & Job Ranking (TF-IDF similarity, persona alignment). ✅ Rollback Safety (graceful error handling to prevent system crashes). 🔥 Once loaded into the GPT Configurator, XMARAX2A will be ready for testing! Let me know if you need additional modifications. 🚀
```python
XMARAX2A Career Pathing Algorithm 1
import json
import networkx as nx
import math
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.spatial.distance import cosine
class XMARAX2A_Core:
def __init__(self, persona_data, job_data):
self.persona_data = persona_data
self.job_data = job_data
self.career_graph = nx.DiGraph()
self.build_career_graph()
def build_career_graph(self):
"""Constructs the directed graph for career pathing."""
for role, connections in self.persona_data["career_paths"].items():
for forward in connections.get("FORWARD_PATH", []):
self.career_graph.add_edge(role, forward)
for backward in connections.get("BACKWARD_PATH", []):
self.career_graph.add_edge(backward, role)
def calculate_tfidf_scores(self, job_description, user_resume):
"""Computes TF-IDF similarity between job description and resume."""
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform([job_description, user_resume])
return 1 - cosine(tfidf_matrix[0].toarray()[0], tfidf_matrix[1].toarray()[0])
def graph_advancement_score(self, target_role):
"""Calculates Graph Advancement Score based on career trajectory."""
if target_role not in self.career_graph:
return 0
try:
path_length = nx.shortest_path_length(self.career_graph, source=self.persona_data["profile"]["candidate_position"], target=target_role)
return 1 / (1 + path_length) # Closer jobs get a higher score
except nx.NetworkXNoPath:
return 0
def industry_transferability_score(self, job_industry):
"""Retrieves industry adaptability score from interaction map."""
return self.persona_data["industry_interaction_map"].get(job_industry, 0.5) # Default 0.5 if undefined
def calculate_weighted_keyword_scores(self, job):
"""Computes the overall weighted score for job ranking."""
tfidf_score = self.calculate_tfidf_scores(job["description"], self.persona_data["resume"])
persona_alignment = self.calculate_persona_alignment(job)
career_progression = self.graph_advancement_score(job["name"])
industry_transferability = self.industry_transferability_score(job["industry"])
return (
(tfidf_score * 0.4) +
(persona_alignment * 0.2) +
(career_progression * 0.2) +
(industry_transferability * 0.1) +
(self.persona_data["user_preferences"]["weight"] * 0.1)
)
def calculate_persona_alignment(self, job):
"""Measures how well a job aligns with persona skills."""
matching_skills = set(job["required_skills"]) & set(self.persona_data["skills"])
return len(matching_skills) / len(job["required_skills"]) if job["required_skills"] else 0
def prioritize_jobs(self):
"""Ranks job opportunities based on computed scores."""
ranked_jobs = []
for job in self.job_data:
score = self.calculate_weighted_keyword_scores(job)
ranked_jobs.append((job["name"], score))
return sorted(ranked_jobs, key=lambda x: x[1], reverse=True)
# Simulated Data for Testing
persona_data = {
"resume": "Experienced AI Writing Evaluator with skills in NLP, Machine Learning, and content analysis.",
"skills": ["AI", "Machine Learning", "NLP", "Content Analysis"],
"profile": {"candidate_position": "AI Writing Evaluator"},
"career_paths": {
"AI Writing Evaluator": {"FORWARD_PATH": ["AI Content Strategist"], "BACKWARD_PATH": ["Content Moderator"]}
},
"industry_interaction_map": {"AI": 1.0, "Tech": 0.8},
"user_preferences": {"weight": 0.8}
}
job_data = [
{"name": "AI Content Strategist", "description": "Looking for an AI expert with NLP skills", "industry": "AI", "required_skills": ["NLP", "Machine Learning"]},
{"name": "Data Scientist", "description": "Seeking a data professional for ML and analytics", "industry": "Tech", "required_skills": ["Python", "Statistics"]}
]
xmarax_core = XMARAX2A_Core(persona_data, job_data)
ranked_jobs = xmarax_core.prioritize_jobs()
print("Ranked Jobs:", ranked_jobs)
Chat
🎯
Job Search Framework & Career Mapping Tool
11 sources
The provided text details the design and implementation of XMARAX, a persona-driven job search system. This system uses a hybrid search algorithm incorporating career mapping, user preferences, and skill analysis to prioritize job opportunities. Key components include a career mapping tool visualizing career paths and a job search framework with tiered search strategies. The system also features JSON integration for configuration and data management, along with detailed algorithms for keyword extraction, weighted scoring, and resume optimization. Finally, patches are proposed to enhance the system's robustness, accuracy, and error handling.
<aside> 💡
Sample resume
</aside>
📍 Wixom, MI | 📧 [email protected] | 📞 734-262-5519
Experienced AI Writing Evaluator and Business Professional with a proven background in AI model evaluation, content analysis, and structured feedback. Expertise in leadership, team development, and data-driven decision-making. Passionate about AI-driven innovation and startup environments, continuously enhancing performance through structured assessments and adaptability.