SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation
This addresses sparsity and cold-start issues for recommender systems, but appears incremental as it builds on existing matrix factorization techniques.
The paper tackles the challenge of sparse, long-tailed datasets and cold-start problems in recommender systems by proposing methods that combine social and sequential information to improve performance, showing effectiveness in large real-world datasets.
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.