Pre-computed Ad Score Caching for Real-Time Bid Latency
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Solution Overview
Problem
In online advertising, advertisers face challenges in delivering optimal ad campaigns due to the limited time available for bid submissions, which leads to suboptimal ad presentation and increased costs, as entities struggle to evaluate numerous potential advertisements within the allotted time.
Innovation Solution
Caching user information and pre-matching advertisements with users allows for reduced latency in responding to bid requests, enabling the evaluation of a larger number of candidate advertisements and improving ad campaign effectiveness by targeting more relevant consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities evaluate each potential advertisement within the allotted time for bid submissions, then bid accuracy improves, but the time required exceeds the allotted bid submission window
Solution Approach 1:
The system pre-calculates and caches probabilistic model scores for multiple candidate advertisements before the bid request arrives. When a bid request is received, the entity can immediately retrieve these pre-computed scores and make a bidding decision within the short time window, thus achieving both accurate bidding and timely submission
Solution Approach 2:
The advertisement evaluation process is divided into two phases: an offline phase where probabilistic models are trained and cached, and an online phase where only retrieval and comparison occur. This segmentation allows computationally intensive operations to be performed in advance, leaving only lightweight operations for real-time bid submission
2Productivity
If entities evaluate a limited number of advertisements to meet the bid submission deadline, then response time is reduced, but ad campaign effectiveness decreases
Solution Approach 1:
Candidate advertisements are pre-identified and scored using probabilistic models before the bid request arrives. This preliminary evaluation expands the pool of evaluated advertisements without increasing real-time processing time, allowing entities to consider more options and improve campaign effectiveness while maintaining fast response speeds
Solution Approach 2:
The system maintains continuous pre-computation of advertisement scores and caches them for rapid retrieval. This continuous preparation ensures that whenever a bid request arrives, the entity is already equipped with evaluated candidate advertisements, eliminating the trade-off between evaluation thoroughness and response speed
3Measurement precision
If probabilistic model evaluation is performed in real-time for each bid request, then targeting precision improves, but computational latency increases
Solution Approach 1:
Probabilistic model evaluations are performed in advance and their results are cached. When a bid request arrives, the system retrieves pre-computed targeting scores rather than performing new evaluations, thus maintaining high targeting precision while reducing computational latency to minimal retrieval times
Solution Approach 2:
The computationally intensive probabilistic model evaluation step is extracted from the real-time bid processing flow and moved to an offline pre-computation phase. Only the lightweight retrieval and application of pre-computed results remain in the real-time path, separating the heavy computational burden from the time-sensitive operation
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for optimization of real-time probabilistic model evaluation for online advertising. In one embodiment, a system may receive a bid request for an ad slot, and may receive cached user information with a first ad and a first ad score, and a second ad and a second ad score. The system may generate a first estimated probability of conversion associated with presentation of the first ad to the user, and may generate a second estimated probability of conversion associated with presentation of the second ad to the user based at least in part on the context information, the second ad identifier, and the second ad score. The system may select either the first ad or the second ad for which to generate a bid amount based at least in part on the first estimated probability of conversion and the second estimated probability of conversion.