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Recently it was shown that the problem of Maximum Inner Product Search (MIPS)is efficient and it admits provably sub-linear hashing algorithms. Asymmetrictransformations before hashing were the key in solving MIPS which was otherwisehard. In the prior work, the authors use asymmetric transformations whichconvert the problem of approximate MIPS into the problem of approximate nearneighbor search which can be efficiently solved using hashing. In this work, weprovide a different transformation which converts the problem of approximateMIPS into the problem of approximate cosine similarity search which can beefficiently solved using signed random projections. Theoretical analysis showthat the new scheme is significantly better than the original scheme for MIPS.Experimental evaluations strongly support the theoretical findings.

Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)