  Top-N recommendation is a challenging problem because complex and sparse
user-item interactions should be adequately addressed to achieve high-quality
recommendation results. The local latent factor approach has been successfully
used with multiple local models to capture diverse user preferences with
different sub-communities. However, previous studies have not fully explored
the potential of local models, and failed to identify many small and coherent
sub-communities. In this paper, we present Local Collaborative Autoencoders
(LOCA), a generalized local latent factor framework. Specifically, LOCA adopts
different neighborhood ranges at the training and inference stages. Besides,
LOCA uses a novel sub-community discovery method, maximizing the coverage of a
union of local models and employing a large number of diverse local models. By
adopting autoencoders as the base model, LOCA captures latent non-linear
patterns representing meaningful user-item interactions within sub-communities.
Our experimental results demonstrate that LOCA is scalable and outperforms
state-of-the-art models on several public benchmarks, by 2.99~4.70% in Recall
and 1.02~7.95% in NDCG, respectively.
