Personalized entity recommendation

TLDR

Network‑based entity recommendation methods that use user or item relationship information are gaining attention, yet most prior work considers only a single relationship type, whereas many real‑world scenarios involve heterogeneous information networks. The study investigates whether incorporating multiple relationship types can improve entity recommendation quality in heterogeneous information networks. We propose a personalized recommendation model that combines heterogeneous relationship information for each user with implicit feedback data to deliver high‑quality recommendations.

Abstract

Among different hybrid recommendation techniques, network-based entity recommendation methods, which utilize user or item relationship information, are beginning to attract increasing attention recently. Most of the previous studies in this category only consider a single relationship type, such as friendships in a social network. In many scenarios, the entity recommendation problem exists in a heterogeneous information network environment. Different types of relationships can be potentially used to improve the recommendation quality. In this paper, we study the entity recommendation problem in heterogeneous information networks. Specifically, we propose to combine heterogeneous relationship information for each user differently and aim to provide high-quality personalized recommendation results using user implicit feedback data and personalized recommendation models.