Hybrid ecommerce recommendation model incorporating product taxonomy and folksonomy

In modern ecommerce platforms, product content information may have two origins: one is tree-structured taxonomy attributes, and the other is free-form folksonomy tags. This paper proposes a hybrid model to incorporate taxonomy and folksonomy information to enhance ecommerce recommendations. It first develops a tree matching algorithm to establish the overall similarity between items, where tag information is integrated for semantic analysis for taxonomy attributes. Next, it proposes a unique random walk model on a heterogeneous graph constructed by user nodes and item nodes and different types of relations - user-item preference and item-item similarity relations. The random walk model is designed to be effective to identify the nearest item nodes for a particular user node, which are seen as the best-fit items for recommendations. Empirical experiments demonstrate that the proposed model improves performance in terms of both recommendation coverage and accuracy, especially for sparse data. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a hybrid model that combines taxonomy and folksonomy information to enhance ecommerce recommendations. By utilizing tree matching algorithm and random walk model on a heterogeneous graph, the proposed model improves recommendation performance in terms of coverage and accuracy, especially for sparse data.