Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing

TL;DR

This paper introduces a method to generate hierarchical item categories from implicit feedback data, enhancing browsing, recommendation diversity, and explainability without manual labeling, especially benefiting small vendors.

Contribution

It presents a novel scalable algorithm, HLTA-Forest, for creating hierarchical item categories solely from implicit feedback data, improving recommendation and browsing experiences.

Findings

Hierarchical categories improve browsing efficiency.

Category-based recommendations are more explainable.

Enhanced diversity in recommendations without losing accuracy.

Abstract

Searching, browsing, and recommendations are common ways in which the "choice overload" faced by users in the online marketplace can be mitigated. In this paper we propose the use of hierarchical item categories, obtained from implicit feedback data, to enable efficient browsing and recommendations. We present a method of creating hierarchical item categories from implicit feedback data only i.e., without any other information on the items like name, genre etc. Categories created in this fashion are based on users' co-consumption of items. Thus, they can be more useful for users in finding interesting and relevant items while they are browsing through the hierarchy. We also show that this item hierarchy can be useful in making category based recommendations, which makes the recommendations more explainable and increases the diversity of the recommendations without compromising much on the accuracy. Item hierarchy can also be useful in the creation of an automatic item taxonomy skeleton by bypassing manual labeling and annotation. This can especially be useful for small vendors. Our data-driven hierarchical categories are based on hierarchical latent tree analysis (HLTA) which has been previously used for text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so that HLTA can be applied to implicit feedback data.