Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

TL;DR

This paper introduces CRAE, a novel deep learning model that combines content sequence generation and collaborative filtering to improve recommendations and fill in missing sequence data.

Contribution

It develops a hierarchical Bayesian denoising recurrent autoencoder tailored for collaborative filtering, integrating sequence modeling with rating prediction.

Findings

Significantly outperforms state-of-the-art methods on recommendation accuracy.

Effectively learns to fill in missing content sequences.

Demonstrates versatility across multiple real-world datasets.

Abstract

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.