# Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation

arXiv: https://arxiv.org/abs/2407.18937 · HTML: https://mlsanity.com/paper/2407.18937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 125 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 30 of 35)

- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Uncovering ChatGPT's Capabilities in Recommender Systems](https://mlsanity.com/paper/2305.02182/) (2023)
- [Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/1906.04477/) (2019)
- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [Generative Recommendation: Towards Next-generation Recommender Paradigm](https://mlsanity.com/paper/2304.03516/) (2023)
- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [A Critical Study on Data Leakage in Recommender System Offline Evaluation](https://mlsanity.com/paper/2010.11060/) (2020)
- [Device-Cloud Collaborative Learning for Recommendation](https://mlsanity.com/paper/2104.06624/) (2021)
- [Fusing Context Into Knowledge Graph for Commonsense Question Answering](https://mlsanity.com/paper/2012.04808/) (2020)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger](https://mlsanity.com/paper/2304.14475/) (2023)
- [EdgeRec: Recommender System on Edge in Mobile Taobao](https://mlsanity.com/paper/2005.08416/) (2020)
- [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023)
- [Intelligent Model Update Strategy for Sequential Recommendation](https://mlsanity.com/paper/2302.07335/) (2023)
- [News Recommender System: A review of recent progress, challenges, and opportunities](https://mlsanity.com/paper/2009.04964/) (2020)
- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [Take a Fresh Look at Recommender Systems from an Evaluation Standpoint](https://mlsanity.com/paper/2210.04149/) (2022)
- [A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions](https://mlsanity.com/paper/2302.04473/) (2023)
- [PBNR: Prompt-based News Recommender System](https://mlsanity.com/paper/2304.07862/) (2023)
- [Addressing Confounding Feature Issue for Causal Recommendation](https://mlsanity.com/paper/2205.06532/) (2022)
- [Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities](https://mlsanity.com/paper/2111.11509/) (2021)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [PORE: Provably Robust Recommender Systems against Data Poisoning Attacks](https://mlsanity.com/paper/2303.14601/) (2023)
- [Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System](https://mlsanity.com/paper/2302.05031/) (2023)
- [Big Data Meets Metaverse: A Survey](https://mlsanity.com/paper/2210.16282/) (2022)
- [STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://mlsanity.com/paper/2306.12232/) (2023)

## Abstract (arXiv)

Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future developments in data, algorithms, and evaluation aspects of RSs. It was found that RSs involve five major research topics, namely algorithmic improvement, domain applications, user behavior & cognition, data processing & modeling, and social impact & ethics. Collaborative filtering and hybrid recommendation techniques are mainstream. The performance of RSs is jointly limited by four types of eight data issues, two types of twelve algorithmic issues, and two evaluation issues. Notably, data-related issues such as cold start, data sparsity, and data poisoning, algorithmic issues like interest drift, device-cloud collaboration, non-causal driven, and multitask conflicts, along with evaluation issues such as offline data leakage and multi-objective balancing, have prominent impacts. Fusing physiological signals for multimodal modeling, defending against data poisoning through user information behavior, evaluating generative recommendations via social experiments, fine-tuning pre-trained large models to schedule device-cloud resource, enhancing causal inference with deep reinforcement learning, training multi-task models based on probability distributions, using cross-temporal dataset partitioning, and evaluating recommendation objectives across the full lifecycle are feasible solutions to address the aforementioned prominent challenges and unlock the power and value of RSs.The collected literature is mainly based on major international databases, and future research will further expand upon it.