# Sequence-Aware Recommender Systems

arXiv: https://arxiv.org/abs/1802.08452 · HTML: https://mlsanity.com/paper/1802.08452/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 30 of 58)

- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation](https://mlsanity.com/paper/2212.08228/) (2022)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [Can Small Language Models be Good Reasoners for Sequential Recommendation?](https://mlsanity.com/paper/2403.04260/) (2024)
- [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023)
- [Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations](https://mlsanity.com/paper/2408.12008/) (2024)
- [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://mlsanity.com/paper/2207.02643/) (2022)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://mlsanity.com/paper/2502.02854/) (2025)
- [Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://mlsanity.com/paper/2205.10759/) (2022)
- [InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2411.09852/) (2024)
- [EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation](https://mlsanity.com/paper/2406.02638/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning](https://mlsanity.com/paper/2305.13747/) (2023)
- [Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges](https://mlsanity.com/paper/2604.02211/) (2026)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)
- [Causal Lifting and Link Prediction](https://mlsanity.com/paper/2302.01198/) (2023)
- [Autoregressive Generation Strategies for Top-K Sequential Recommendations](https://mlsanity.com/paper/2409.17730/) (2024)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://mlsanity.com/paper/2305.10824/) (2023)
- [UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations](https://mlsanity.com/paper/2406.18470/) (2024)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System](https://mlsanity.com/paper/2103.08458/) (2021)
- [Summary Markov Models for Event Sequences](https://mlsanity.com/paper/2205.03375/) (2022)
- [KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting](https://mlsanity.com/paper/2102.07825/) (2021)
- [Analyzing the Effect of Sampling in GNNs on Individual Fairness](https://mlsanity.com/paper/2209.03904/) (2022)

## Abstract (arXiv)

Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.