On Recommending Category: A Cascading Approach

TL;DR

This paper introduces CCRec, a cascading category recommendation model using a variational autoencoder to improve category-level recommendations in e-commerce, addressing limitations of adapting item-level models.

Contribution

It proposes a novel cascading model with VAE that effectively encodes item-level data for better category recommendations, filling a gap in existing methods.

Findings

CCRec outperforms traditional item-level recommendation models.

The model effectively captures category-level preferences.

Experiments demonstrate improved recommendation accuracy.

Abstract

Recommendation plays a key role in e-commerce, enhancing user experience and boosting commercial success. Existing works mainly focus on recommending a set of items, but online e-commerce platforms have recently begun to pay attention to exploring users' potential interests at the category level. Category-level recommendation allows e-commerce platforms to promote users' engagements by expanding their interests to different types of items. In addition, it complements item-level recommendations when the latter becomes extremely challenging for users with little-known information and past interactions. Furthermore, it facilitates item-level recommendations in existing works. The predicted category, which is called intention in those works, aids the exploration of item-level preference. However, such category-level preference prediction has mostly been accomplished through applying item-level models. Some key differences between item-level recommendations and category-level recommendations are ignored in such a simplistic adaptation. In this paper, we propose a cascading category recommender (CCRec) model with a variational autoencoder (VAE) to encode item-level information to perform category-level recommendations. Experiments show the advantages of this model over methods designed for item-level recommendations.