Search Queries Anomaly Detection means identifying queries that are outliers according to their performance metrics. It is valuable for businesses to spot potential issues or opportunities, such as unexpectedly high or low CTRs.
Below is the process we can follow for the task of Search Queries Anomaly Detection:
Gather historical search query data from the source, such as a search engine or a website’s search functionality.
Conduct an initial analysis to understand the distribution of search queries, their frequency, and any noticeable patterns or trends.
Create relevant features or attributes from the search query data that can aid in anomaly detection.
Choose an appropriate anomaly detection algorithm. Common methods include statistical approaches like Z-score analysis and machine learning algorithms like Isolation Forests or One-Class SVM.
Train the selected model on the prepared data.
Apply the trained model to the search query data to identify anomalies or outliers.