# Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

arXiv: https://arxiv.org/abs/1905.01997 · HTML: https://mlsanity.com/paper/1905.01997/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://mlsanity.com/paper/2205.10759/) (2022)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Improving Next-Application Prediction with Deep Personalized-Attention Neural Network](https://mlsanity.com/paper/2111.11296/) (2021)
- [Search Intenion Network for Personalized Query Auto-Completion in E-Commerce](https://mlsanity.com/paper/2403.02609/) (2024)
- [Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models](https://mlsanity.com/paper/2412.16933/) (2024)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [A Sequence-Aware Recommendation Method Based on Complex Networks](https://mlsanity.com/paper/2210.07814/) (2022)

## Abstract (arXiv)

In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.