Survival of the fittest still applies.
Your product is being tested-your competitor is analysing sentiment and emojis. Choose your edge
Challenges to Recommender system Optimisation
Causality-driven recommendation optimization
I have data, but my recommender model often focuses on correlations rather than causality, leading to inaccurate recommendations that don't account for the true drivers of user behavior.
Real-time recommendation performance bottlenecks
I have real-time user actions, but my system lacks the processing speed to deliver relevant recommendations in dynamic environments.
Over personalization & lack of recommendation diversity
I have user interaction data, but my system sometimes becomes too focused on past behavior, limiting its ability to suggest diverse options.
AI-driven causal Inference testing to help you dive deeper into user behavior drivers instead of surface-level correlations
Performance testing with load simulation frameworks to ensure your system can handle high-throughput, low-latency processing
Bias and Overfitting tests on your recommendation engine to identify and mitigate algorithmic stagnation.
Segment-specific regression and compatibility testing to ensure algorithms adapt seamlessly to various user demographics
