AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System

TL;DR

AdaptJobRec is a novel conversational career recommendation system that intelligently balances response speed and accuracy by using autonomous agents and query complexity detection, leading to faster and more precise recommendations.

Contribution

It introduces the first agentic conversational job recommendation system that dynamically adapts its processing based on query complexity to improve efficiency and effectiveness.

Findings

Reduces response latency by up to 53.3%

Improves recommendation accuracy significantly

Effectively handles both simple and complex queries

Abstract

In recent years, recommendation systems have evolved from providing a single list of recommendations to offering a comprehensive suite of topic focused services. To better accomplish this task, conversational recommendation systems (CRS) have progressed from basic retrieval augmented LLM generation to agentic systems with advanced reasoning and self correction capabilities. However, agentic systems come with notable response latency, a longstanding challenge for conversational recommendation systems. To balance the trade off between handling complex queries and minimizing latency, we propose AdaptJobRec, the first conversational job recommendation system that leverages autonomous agent to integrate personalized recommendation algorithm tools. The system employs a user query complexity identification mechanism to minimize response latency. For straightforward queries, the agent directly selects the appropriate tool for rapid responses. For complex queries, the agent uses the memory processing module to filter chat history for relevant content, then passes the results to the intelligent task decomposition planner, and finally executes the tasks using personalized recommendation tools. Evaluation on Walmart's real world career recommendation scenarios demonstrates that AdaptJobRec reduces average response latency by up to 53.3% compared to competitive baselines, while significantly improving recommendation accuracy.