# Towards Neural Mixture Recommender for Long Range Dependent User Sequences

arXiv: https://arxiv.org/abs/1902.08588 · HTML: https://mlsanity.com/paper/1902.08588/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 15 of 15)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Zero-Shot Recommender Systems](https://mlsanity.com/paper/2105.08318/) (2021)
- [Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection](https://mlsanity.com/paper/2201.01004/) (2022)
- [Improving Training Stability for Multitask Ranking Models in Recommender Systems](https://mlsanity.com/paper/2302.09178/) (2023)
- [Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://mlsanity.com/paper/2308.12256/) (2023)
- [Latent User Intent Modeling for Sequential Recommenders](https://mlsanity.com/paper/2211.09832/) (2022)
- [Context Uncertainty in Contextual Bandits with Applications to Recommender Systems](https://mlsanity.com/paper/2202.00805/) (2022)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [Recency Dropout for Recurrent Recommender Systems](https://mlsanity.com/paper/2201.11016/) (2022)
- [NxtPost: User to Post Recommendations in Facebook Groups](https://mlsanity.com/paper/2202.03645/) (2022)
- [EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention](https://mlsanity.com/paper/2403.17729/) (2024)
- [Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decoding](https://mlsanity.com/paper/2511.10492/) (2025)
- [Neural Hierarchical Factorization Machines for User's Event Sequence Analysis](https://mlsanity.com/paper/2112.15292/) (2021)
- [Influential Recommender System](https://mlsanity.com/paper/2211.10002/) (2022)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)

## Abstract (arXiv)

Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application contexts require adapting to diverse behaviors. In this paper we examine how to build a model that can make use of different temporal ranges and dynamics depending on the request context. We begin with the analysis of an anonymized Youtube dataset comprising millions of user sequences. We quantify the degree of long-range dependence in these sequences and demonstrate that both short-term and long-term dependent behavioral patterns co-exist. We then propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution to deal with both short-term and long-term dependencies. Our approach employs a mixture of models, each with a different temporal range. These models are combined by a learned gating mechanism capable of exerting different model combinations given different contextual information. In empirical evaluations on a public dataset and our own anonymized YouTube dataset, M3 consistently outperforms state-of-the-art sequential recommendation methods.