AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
This work addresses latency issues in conversational job recommendation systems, offering a domain-specific improvement for users and platforms like Walmart.
The paper tackled the problem of high response latency in conversational recommendation systems by proposing AdaptJobRec, a system that uses an autonomous agent to integrate personalized recommendation tools, resulting in a 53.3% reduction in average response latency and improved recommendation accuracy in job recommendation scenarios.
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.