  Neighbourhood-based Collaborative Filtering (CF) has been applied in the
industry for several decades, because of the easy implementation and high
recommendation accuracy. As the core of neighbourhood-based CF, the task of
dynamically maintaining users' similarity list is challenged by cold-start
problem and scalability problem. Recently, several methods are presented on
solving the two problems. However, these methods applied an $O(n^2)$ algorithm
to compute the similarity list in a special case, where the new users, with
enough recommendation data, have the same rating list. To address the problem
of large computational cost caused by the special case, we design a faster
($O(\frac{1}{125}n^2)$) algorithm, TwinSearch Algorithm, to avoid computing and
sorting the similarity list for the new users repeatedly to save the
computational resources. Both theoretical and experimental results show that
the TwinSearch Algorithm achieves better running time than the traditional
method.
