Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
This work addresses the MIPS problem, which is crucial for applications like recommendation systems and machine learning, by providing a more efficient hashing-based solution, though it is incremental as it builds on existing asymmetric locality sensitive hashing approaches.
The paper tackles the Maximum Inner Product Search (MIPS) problem by introducing a new asymmetric transformation that converts it into a cosine similarity search, which is solved using signed random projections, resulting in significant improvements over prior methods as supported by theoretical and experimental evidence.
Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing. In this work, we provide a different transformation which converts the problem of approximate MIPS into the problem of approximate cosine similarity search which can be efficiently solved using signed random projections. Theoretical analysis show that the new scheme is significantly better than the original scheme for MIPS. Experimental evaluations strongly support the theoretical findings.