Evaluating unsupervised learning models

Techniques described herein include systems and methods for evaluating an unsupervised machine learning model. In some embodiments, the system identifies item-to-item similarity values based on historical transaction data. The system may also generate collection data for a number of users based on the historical transaction data. Similarity matrices may be created for each pair of users that include rows associated with a first collection and columns associated with a second collection. Each data field in the similarity matrix may indicate an item-to-item similarity value as identified by the system. In some embodiments, a similarity score may be calculated for the user pair based on the item-to-item similarity values included in the similarity matrix. In some embodiments, the system may generate a graphical summary representation of the similarity matrix.