2018/01/08 by Zhu Han, Han Zhu, Xiang Li +5 · 1 voice · 295 citations
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Artificial neural network #Computer science #Data mining #Deep learning #Deep neural networks #Information retrieval #Logarithm #Machine learning #Recommender Systems and Techniques #Recommender system #Topic Modeling #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.1145/3219819.3219826
Accepted by KDD 2018
openalex publication_date 2018/07/19 · arxiv created 2018/12/21 · arxiv updated 2018/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.