Joint User Modeling Across Aligned Heterogeneous Sites Using Neural Networks

Abstract

The quality of user modeling is crucial for personalized recommender systems. Traditional within-site recommender systems aim at modeling user preferences using only actions within target site, thus suffer from cold-start problem. To alleviate such problem, researchers propose cross-domain models to leverage user actions from other domains within same site. Joint user modeling is later proposed to further integrate user actions from aligned sites for data enrichment. However, there are still limitations in existing works regarding the modeling of heterogeneous actions, the requirement of full alignment and the design of preferences coupling. To tackle these, we propose JUN: a joint user modeling framework using neural network. We take advantage of neural network’s capability of capturing different data types and its ability for mining high-level non-linear correlations. Specifically, in additional to site-specific preferences models, we further introduce an auxiliary neural network to transfer knowledge between sites by fine-tuning the user embeddings using alignment information. We adopt JUN for item-based and text-based site to demonstrate its performance. Experimental results indicate that JUN outperforms both within-site and cross-site models. Specifically, JUN achieves relative improvement of 2.96% and 2.37% for item-based and text-based sites (5.77% and 13.54% for cold-start scenarios). Besides performance gain, JUN also achieves great generality and significantly extends the use scenarios.