Exploring the Power of AI in Advertising 🚀
Ever wondered how companies use AI and your data to enhance sales and deliver personalized experiences? 🤔
Developing a Clever AI Model 🧠
Inspired by successful strategies used by tech giants like Facebook, my friend and I built a sophisticated AI model. This smart creation dives deep into user behavior, capturing valuable insights to create personalized ads and understand individual preferences.
Maximizing User Engagement 💥
Our main goal was to maximize clicks on our ads and provide an exceptional user experience. We utilized the Hungarian Algorithm to simulate a matching problem, allowing us to strategically select captivating ad-slot combinations based on user preferences.
Navigating the Competitive Auctions 💰
In the highly competitive world of online advertising, we knew we had to be smart to secure prime ad slots. Websites often hold auctions, granting slots to the highest bidder. To stay within budget and outperform competitors, we implemented the VGC (Virtual Generalized Second Price) mechanism. Using a Gaussian process Thompson Sampling approach, our AI learned optimal bidding strategies and daily budgets, ensuring wins in the auctions while being mindful of our limited budget. 💡
Learning Click Probabilities and Context Generation 🎯
To continuously improve our advertising campaigns, we employed combinatorial bandit algorithms to estimate click probabilities for our ads. By bidding truthfully and analyzing outcomes, we refined our understanding of user behavior. Additionally, we developed a context generation algorithm that used collected data to make better allocation decisions.
Optimizing the Campaign with Budget Considerations 💪
Considering our budget limitations, we incorporated the classical VCG auction mechanism to compute payments based on social welfare optimization. This allowed us to make informed bidding and budgeting decisions. We designed an algorithm to optimize our campaign, choosing the best bids and budgets for each subcampaign on a daily basis.
Simultaneous Learning for Publishers and Advertisers 📚
Throughout our journey, we discovered the value of simultaneous learning for publishers and advertisers. By separately assessing cumulative regret for both parties, we gained insights into the impact of our learning process. This helped us identify opportunities for collaboration and improvement, benefiting publishers and advertisers in the dynamic landscape of online advertising.
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We've shared a detailed article ✍️ for those eager to dive deeper into the intricacies. If you're interested in exploring the code and playing with it yourself, feel free to download it and have fun!