Optimizing Azure Cognitive Search for Improved Relevance Scores

ACS (Automated Content Selection) is a cloud-based service designed to provide users with relevant search results based on their preferences. While ACS has been successful in its default settings, there is room for improvement by customizing the weights and using user data to optimize engagement. By leveraging historical data on users’ clicks and search sessions, ACS can determine the most effective set of weights to maximize user interaction with returned items.
The optimization process involves defining an optimization function that measures the relevance of each item based on its click-through rate (CTR). The quality of the items returned by ACS can be evaluated using normalized discounted cumulative gain (nDCG), which compares the ranking of items to their ideal ranking. By adjusting the weights and incorporating boosting features, such as popularity and transactability, ACS can create a personalized ranking for each user based on their unique preferences and behavior.
The article demystifies complex concepts by using everyday language and engaging metaphors, such as comparing ACS to a black-box function that takes in user queries and returns relevant items. By balancing simplicity and thoroughness, the summary captures the essence of the article without oversimplifying the concepts.