A Trajectory-Oriented Locality-Sensitive Hashing Method for User Identification

User identification across social sites, which benefits many applications, has recently been attracting considerable attention. Most existing methods focused more on the effectiveness of user identification, rather than on efficiency. Matching as many cross-site user accounts as possible, which causes very high computation overhead posed by the full-scale pairwise comparisons, remains unsolved, especially when the number of users reaches tens of millions or more. To address this issue, we present a novel
l
ocality-
s
ensitive
h
ashing method for
u
ser
i
dentification (UI-LSH), which consists of four components. 1) It involves embedding stay points into vectors, 2) and constructing locality-sensitive hashing families suitable for stay points. 3) It presents a method for projecting stay points into hash buckets that ensures the close stay points are placed in the same bucket with high probability. 4) It constructs the candidate user pairs based on the projection results. The experiments on three ground-truth datasets show that our method reduces the number of user pairs to be compared by as much as 81.87%, 67.68%, and 63.15%, respectively. Overall, UI-LSH holds great promise for significantly improving the efficiency of user identification.