Abstract
The manual inspection and evaluation of the performance of artificial intelligence (AI) agents in complex computer systems may be challenging, especially in computer systems that include recommendation engines and ranking models. The manual inspection and evaluation of the performance of the AI agents in complex computer systems may be a tedious, subjective, and error-prone process. Determining the specific contributions of a multitude of individual signals to a final rank may be difficult when using human inspection, and in addition or in the alternative, performance drift from specific goals may go unnoticed.
This disclosed technology implements a recommendation quality evaluation artificial intelligence (AI) agent for a recommendation system included in a computer system. A recommendation process that recommends content (e.g., media content) for consumption by a user of the computer system may use the recommendation quality evaluation AI agent to provide an automated and insightful analysis of recommended content to the user as compared to the current manual process that may be subjective and labor-intensive. For example, evaluation scenarios may run on test accounts where each test account represents a different persona of a user of the computer system.
Each evaluation scenario may capture detailed runtime logs of interactions of a user of the test account with the computer system while interacting with an application program running on the computer system along with the corresponding source code to extract the backend signals and remote procedure calls (RPCs) that influenced a particular recommendation to the user. The recommendation quality evaluation AI agent may analyze contextual data, including the persona of the user and historical behavior of the user, along with artificial intelligence (AI) recommendation model details to determine specific ranking drivers for use in ranking and/or recommending content. Discrepancies between the generated content recommendations and defined guidelines, such as taste or popularity drift, may be flagged for manual verification.
The implementation of the recommendation quality evaluation AI agent may act as a guardrail to catch pitfalls before deploying the recommendation system into production. The use of a recommendation quality evaluation AI agent may provide greater transparency to the recommendation process for the recommendation system. The use of a recommendation quality evaluation AI agent may improve the efficiency of quality assurance workflows by eliminating manual efforts that may lead towards confirmed failures.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chatterjee, Tamojit; Mishra, Kanishk; Murugesan, Sundaramoorthy; and Kapoor, Ankur, "Recommendation Quality Evaluation Artificial Intelligence Agent", Technical Disclosure Commons, (March 05, 2026)
https://www.tdcommons.org/dpubs_series/9455