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

arXiv: https://arxiv.org/abs/1708.04497 · HTML: https://mlsanity.com/paper/1708.04497/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 8 of 8)

- [Lightweight Compositional Embeddings for Incremental Streaming Recommendation](https://mlsanity.com/paper/2202.02427/) (2022)
- [RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://mlsanity.com/paper/2309.10469/) (2023)
- [Augmenting Textual Generation via Topology Aware Retrieval](https://mlsanity.com/paper/2405.17602/) (2024)
- [Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning](https://mlsanity.com/paper/2408.07091/) (2024)
- [Personalized Programming Guidance based on Deep Programming Learning Style Capturing](https://mlsanity.com/paper/2403.14638/) (2024)
- [Personalized Communication Skills for Agentic Recommender Systems](https://mlsanity.com/paper/2608.08417/) (2026)
- [CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2608.09580/) (2026)
- [TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs](https://mlsanity.com/paper/2609.08226/) (2026)

## Abstract (arXiv)

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.