SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

Chenwei Cai, Ruining He, Julian McAuley

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1476-1482. https://doi.org/10.24963/ijcai.2017/204

Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.
Keywords:
Machine Learning: Data Mining
Machine Learning: Machine Learning
Multidisciplinary Topics and Applications: AI and Social Sciences
Combinatorial & Heuristic Search: Modeling