Unsupervised learning applies to information retrieval (IR) by allowing the system to identify patterns and structure in data without requiring labeled training data. In IR, this can help to organize, cluster, and categorize large datasets without the need for predefined labels or manual tagging.
For example, in a document retrieval system, unsupervised learning techniques such as clustering or topic modeling can group documents with similar content together, helping the system recommend relevant documents based on content similarity rather than specific labels. This is especially useful when there is a large amount of unstructured data.
Unsupervised learning can also improve query expansion, where the system automatically identifies related terms or phrases to improve the retrieval of relevant documents. By analyzing patterns in the data itself, unsupervised learning can uncover hidden structures and relationships, leading to more efficient and effective information retrieval.