This page organizes papers related to artificial intelligence published around the world. This page is summarized using Google Gemini and is operated on a non-profit basis. The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.
AdaptJobRec is the first interactive job recommendation system that minimizes response latency by leveraging a mechanism that identifies user query complexity. For simple queries, it directly selects the appropriate tool, while for complex queries, it utilizes a memory processing module, intelligent job decomposition planning, and personalized recommendation tools. In a real-world job recommendation scenario at Walmart, it significantly improved recommendation accuracy, reducing average response latency by up to 53.3% compared to competing systems.
Takeaways, Limitations
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We present a novel approach to balancing response latency and complex query processing in conversational recommendation systems.
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System design that dynamically selects tools based on user query complexity.
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Performance validation in a real-world environment (Walmart scenario).
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Limitations:
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Further research is needed to determine generalizability, with validation in a specific setting (Walmart hiring).
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The performance of the system depends on the accuracy of the query complexity identification mechanism.
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Further analysis is needed to determine the cause of the improved accuracy.
