MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs
This addresses matrix completion with graph side information, which is incremental as it builds on existing methods by incorporating dual graphs.
The paper tackles the problem of matrix completion using social and item similarity graphs, proposing MC2G, an efficient algorithm that runs in quasilinear time and is parameter-free, with experiments showing it outperforms other state-of-the-art methods.
In this paper, we design and analyze MC2G (Matrix Completion with 2 Graphs), an algorithm that performs matrix completion in the presence of social and item similarity graphs. MC2G runs in quasilinear time and is parameter free. It is based on spectral clustering and local refinement steps. The expected number of sampled entries required for MC2G to succeed (i.e., recover the clusters in the graphs and complete the matrix) matches an information-theoretic lower bound up to a constant factor for a wide range of parameters. We show via extensive experiments on both synthetic and real datasets that MC2G outperforms other state-of-the-art matrix completion algorithms that leverage graph side information.