Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data

TL;DR

This paper introduces a scalable collaborative filtering method using denoising auto-encoders trained on corrupted market basket data, enabling effective missing item prediction and basket generation.

Contribution

It presents a novel neural network-based approach for collaborative filtering that handles large datasets and provides both missing item prediction and basket generation capabilities.

Findings

Effective in predicting missing items in baskets

Scalable approach suitable for medium-to-large datasets

Generates realistic basket data for simulation and analysis

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.