  As online dating has become more popular in the past few years, an efficient
and effective algorithm to match users is needed. In this project, we proposed
a new dating matching algorithm that uses Kendall-Tau distance to measure the
similarity between users based on their ranking for items in a list. (e.g.,
their favourite sports, music, etc.) To increase the performance of the search
process, we applied a tree-based searching structure, Cascading Metric Tree
(CMT), on this metric. The tree is built on ranked lists from all the users;
when a query target and a radius are provided, our algorithm can return users
within the radius of the target. We tested the scaling of this searching method
on a synthetic dataset by varying list length, population size, and query
radius. We observed that the algorithm is able to query the best matching
people for the user in a practical time, given reasonable parameters. We also
provided potential future improvements that can be made to this algorithm based
on the limitations. Finally, we offered more use cases of this search structure
on Kendall-Tau distance and new insight into real-world applications of
distance search structures.
