Title: Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data,
Abstract: Recommender systems (RS) help users navigate large sets of items in the
search for "interesting" ones. One approach to RS is Collaborative Filtering
(CF), which is based on the idea that similar users are interested in similar
items. Most model-based approaches to CF seek to train a
machine-learning/data-mining model based on sparse data; the model is then used
to provide recommendations. While most of the proposed approaches are effective
for small-size situations, the combinatorial nature of the problem makes it
impractical for medium-to-large instances. In this work we present a novel
approach to CF that works by training a Denoising Auto-Encoder (DAE) on
corrupted baskets, i.e., baskets from which one or more items have been
removed. The DAE is then forced to learn to reconstruct the original basket
given its corrupted input. Due to recent advancements in optimization and other
technologies for training neural-network models (such as DAE), the proposed
method results in a scalable and practical approach to CF. The contribution of
this work is twofold: (1) to identify missing items in observed baskets and,
thus, directly providing a CF model; and, (2) to construct a generative model
of baskets which may be used, for instance, in simulation analysis or as part
of a more complex analytical method.