A Framework for Dynamic ANN Index Lifecycle Management in Ad Retrieval

The digital advertising landscape requires exceptional scale and efficiency in candidate retrieval systems, where Approximate Nearest Neighbor indexes function as the core technology facilitating real-time ad matching across extensive inventories. Dynamic advertising catalogs face distinct challenges due to ongoing changes from campaign launches, budget modifications, and creative updates, requiring ANN indexes that can manage frequent insertions, deletions, and changes while ensuring optimal query performance. Current dynamic ANN implementations experience a gradual performance decline as update operations build up, resulting in heightened query latency and diminished retrieval accuracy that directly affects system efficiency. The suggested framework tackles these issues using a thorough method that merges smart index selection techniques with advanced lifecycle management strategies. The selection element assesses candidate ANN algorithms based on workload-specific traits, including degradation resistance as a key factor in addition to conventional static performance measurements. The management part employs a dual-layer architecture that differentiates real-time updates from batch optimizations, allowing for instant responsiveness while maintaining long-term performance traits. At the core of this framework is a re-indexing policy that is aware of degradation, which observes performance indicators and initiates reconstruction actions using predictive models and adjustable thresholds. Experimental validation shows the framework's efficacy in various scenarios that reflect production advertising environments, sustaining steady performance over long operational durations, whereas traditional methods need regular manual input. The framework allows for consistently high recall and minimal latency during millions of update operations, greatly surpassing conventional update-and-ignore methods typically used in dynamic indexing systems.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.