  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.
