A channel coding perspective of recommendation systems
Sundar Aditya, Onkar Dabeer, Bikash Kumar Dey · 2009
Motivated by recommendation systems, we consider the problem of estimating block constant binary matrices (of size m × n) from sparse and noisy observations. The observations are obtained from the underlying block constant matrix after unknown row and column permutations, erasures, and errors. We derive upper and lower bounds on the achievable probability of error. For fixed erasure and error probability, we show that there exists a constant C1such that if the cluster sizes are less than C1ln(mn), then for any algorithm the probability of error approaches one as m, n ¿ ¿. On the other hand, we show that a simple polynomial time algorithm gives probability of error diminishing to zero provided the cluster sizes are greater than C2ln(mn) for a suitable constant C2.