# Sequence-Aware Factorization Machines for Temporal Predictive Analytics

arXiv: https://arxiv.org/abs/1911.02752 · HTML: https://mlsanity.com/paper/1911.02752/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation](https://mlsanity.com/paper/2109.02046/) (2021)
- [Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution](https://mlsanity.com/paper/2311.11235/) (2023)
- [Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model](https://mlsanity.com/paper/2410.23994/) (2024)
- [Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph](https://mlsanity.com/paper/2101.00752/) (2021)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [Bridging User Dynamics: Transforming Sequential Recommendations with Schr"odinger Bridge and Diffusion Models](https://mlsanity.com/paper/2409.10522/) (2024)
- [Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations](https://mlsanity.com/paper/2212.05720/) (2022)
- [Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling](https://mlsanity.com/paper/2104.01716/) (2021)
- [Factorized Structured Regression for Large-Scale Varying Coefficient Models](https://mlsanity.com/paper/2205.13080/) (2022)
- [Attribute-aware Explainable Complementary Clothing Recommendation](https://mlsanity.com/paper/2107.01655/) (2021)
- [Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs](https://mlsanity.com/paper/2109.07571/) (2021)
- [Spatial-Temporal Meta-path Guided Explainable Crime Prediction](https://mlsanity.com/paper/2205.01901/) (2022)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2602.08638/) (2026)
- [TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation](https://mlsanity.com/paper/2604.09439/) (2026)

## Abstract (arXiv)

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics. As the volume of web-scale data grows exponentially over time, sparse predictive analytics inevitably involves dynamic and sequential features. However, existing FM-based models assume no temporal orders in the data, and are unable to capture the sequential dependencies or patterns within the dynamic features, impeding the performance and adaptivity of these methods. Hence, in this paper, we propose a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. As static features (e.g., user gender) and dynamic features (e.g., user interacted items) express different semantics, we innovatively devise a multi-view self-attention scheme that separately models the effect of static features, dynamic features and the mutual interactions between static and dynamic features in three different views. In SeqFM, we further map the learned representations of feature interactions to the desired output with a shared residual network. To showcase the versatility and generalizability of SeqFM, we test SeqFM in three popular application scenarios for FM-based models, namely ranking, classification and regression tasks. Extensive experimental results on six large-scale datasets demonstrate the superior effectiveness and efficiency of SeqFM.