Cold-Start Strategy Overview

• Cold-Start Strategy is a set of algorithmic and operational solutions that address performance degradation when initial data is limited, widely applied in recommender systems and serverless computing.
• The approach employs methods like graph analysis, meta-learning, and self-supervised pre-training to optimize exposure, reduce latency, and improve prediction accuracy in sparse data environments.
• Specialized techniques such as pool-based pre-warming and predictive autoscaling in cloud services demonstrate significant improvements in resource efficiency and system responsiveness.

A cold-start strategy refers to a set of algorithmic, system-level, or operational solutions designed to mitigate the performance degradation that occurs when a system, model, or process is faced with insufficient or missing initial data. The cold-start problem is endemic in domains such as recommender systems—where new items and users initially lack interaction histories—as well as in serverless computing, LLM serving, online control systems, and active learning frameworks. A unifying challenge is that key components of the system must make accurate predictions, recommendations, or allocation decisions despite limited or no historical data or cached resources. Cold-start strategies use architectural, statistical, and learning-based mechanisms to optimize exposure, minimize latency, and accelerate adaptation under such circumstances.

1. Mathematical Foundations of the Cold-Start Problem

The formalization of the cold-start problem typically begins with characterizing the data structure and system constraints under which recommendations or predictions are made despite sparsity. In recommender systems, the interaction network is represented as a bipartite graph $G(U, O, E)$ with adjacency matrix $A$, where $U$ indicates the user set, $O$ the item set, and $E$ the observed user–item interactions. The degree $k_i$ (user) or $k_\alpha$ (item) quantifies connectivity. The mathematical cold-start challenge is often cast as an optimization problem: for a new entity (e.g., item $O_\eta$) and a resource constraint $R$, select $R$ connections so as to maximize a downstream utility function—such as the number of recommendation lists featuring $O_\eta$:

$$H = \sum_{i=1}^n \delta_i(R),\quad\text{where}\ \delta_i(R) = \begin{cases} 1 & O_\eta\ \text{in}\ U_i\text{'s top-}L\text{ list}\ 0 & \text{otherwise} \end{cases}$$

The cold-start problem in serverless computing, by contrast, is modeled by response-time equations that isolate the additional delay from on-demand resource instantiation:

$$T_{i}^{\mathrm{Cold}} = t_i + T_i,\quad\text{where}\ t_i\ \text{is mean serving time,\ }T_i\ \text{is cold-start overhead}$$

Capacity planning for cloud systems incorporates stochastic and queueing models (e.g., M/M/1/setup, CTMCs, Layered Queueing Networks), with the overall system-level optimization formulated to minimize cost or latency subject to SLA constraints.

2. Cold-Start Strategies in Recommender Systems

2.1 Graph and Network-Based Targeting

In item-based collaborative filtering (ICF), the system computes recommendations from observed similarity (e.g., cosine similarity between item vectors), which is relationally driven by the bipartite user–item topology. Empirical and theoretical analysis demonstrates that attaching a new item to low-degree (less active) users, as opposed to high-degree (highly active) "influencers," leads to higher inclusion rates of the item in other users’ top-$L$ recommendation lists. The mechanism underpinning this effect is the disassortative nature of real-world user–item networks: high-degree users mostly collect niche items, while low-degree users engage with popular, high-visibility items. Thus, cold-start strategies that specifically connect new items to less active users leverage network topology to improve the initial spread of the item within recommendation outputs (Liu et al., 2014).

2.2 Meta-Learning and Evidence Selection

Meta-learning frameworks (e.g., MeLU) treat each new user's preference adaptation as a separate task and utilize few-shot learning algorithms such as MAML. The model’s architecture typically includes embedding layers for user/item features and decision-making (deep) layers. Rapid personalization is achieved via local updates to portions of the model (e.g., fully connected layers), giving strong initial performance with minimal user data. A key innovation is the selection of evidence candidates: items to be presented to the user are chosen by maximizing a joint criterion based on the magnitude of induced parameter gradient during personalization and item popularity, ensuring informativeness and user familiarity. This evidence selection improves mean absolute error and ranking metrics for cold-start users/items, as seen in comprehensive benchmark evaluations (Lee et al., 2019).

2.3 Graph Neural Networks and Knowledge Graph Pseudo-Labelling

GNN-based approaches leverage side-information from knowledge graphs (KGs) to enrich sparse user–item matrices. Adopting pseudo-labelling, unobserved user–item pairs are not treated as automatic negatives; instead, soft positive labels are imputed based on KG paths and model prediction. The combination of targeted pseudo-label selection (guided by meta-paths in the KG) and popularity-aware negative sampling mitigates the bias toward already popular items, thus improving cold-start recommendations for new users/items. The GNN aggregation uses user-specific adjacency matrices, and the final loss combines observed, pseudo-positive, and popularity-controlled negatives (Togashi et al., 2020).

2.4 Self- and Contrastive Supervised Pre-Training

Hybrid self-supervised pre-training strategies, such as those involving GNNs and Transformer encoders, address cold-start scenarios by capturing both short-range and long-range dependencies in user–item graphs. Embedding reconstruction tasks cover intra-subgraph structure, while contrastive learning (via data augmentations and positive/negative pair generation) enables modeling of inter-subgraph (cross-user/item) relationships. Meta-learning simulations with artificially sparse support sets train the system to infer robust embeddings from minimal evidence, increasing cold-start robustness and recommendation quality under extreme data sparsity (Hao et al., 2021).

2.5 Content-Side and Alignment Approaches

Alignment models incorporate content metadata to supplement collaborative filtering under severe sparsity. The MARec framework regularizes the similarity space learned from observed clicks by aligning it with that induced from item metadata, using an explicit alignment term in the loss. Smoothed cosine similarity functions (with regularization that down-weights popular items) ensure that cold-start items, which lack enough interactions for purely data-driven latent embeddings, inherit structure from side-content (e.g., textual features, LLM embeddings, or image vectors). This approach substantially boosts ranking metrics in cold-start settings (Monteil et al., 20 Apr 2024).

3. Cold-Start Strategies in Serverless Computing and Cloud Services

3.1 Pool-based Pre-Warming

Maintaining a limited pool of pre-initialized "warm" containers (or pods) is a direct and effective means to mitigate cold-starts in serverless platforms. Pools are managed as dedicated resources within orchestrators (e.g., Knative), with autoscaler logic intercepting scale-up events to first migrate available warm pods rather than triggering full container initialization. Experimental results show that, even with a single warm pod, the 99th percentile response time (P99) can be reduced by up to 85%. However, the marginal benefit decreases when many services share a small pool, necessitating tuning of pool sizes with respect to expected concurrency (Lin et al., 2019).

3.2 Forecasting and Predictive Autoscaling

Time series prediction models (e.g., SARIMA, Temporal Convolutional Networks) are employed to anticipate function invocation rates and proactively pre-provision containers in advance of expected load spikes. SARIMA, with its ability to capture both autoregressive trends and periodic cycles, allows the implementation of prediction-based autoscalers (PBA) that outperform conventional CPU/memory-driven autoscalers (HPA), yielding both lower response times and up to 18% resource reduction (Jegannathan et al., 2022). Deep learning predictors (TCNs) further enhance the forecast horizon, facilitating ensemble orchestration policies that act at both the infrastructure-level (provisioning worker nodes) and function-level (code optimization and dependency caching), with superior explained variance (e.g., MAPE of 15.79 and Spearman 0.92) compared to ARIMA-based baselines (Nguyen, 2023).

3.3 Differentiated Scheduling and Resource-Aware Provisioning

Rule-based schedulers such as SPES analyze historical invocation patterns—characterizing waiting time (WT), active time (AT), and burst structure—to categorize functions and implement differentiated pre-load/eviction strategies. For instance, "regular" functions are pre-loaded just before their predicted invocations, and "idle" or "correlated" functions are handled via co-occurrence metrics with related workloads. By tuning parameters such as pre-warm windows and eviction thresholds, SPES achieves an explicit linear resource-latency trade-off, evidenced by a 49.77% reduction in 75th-percentile cold-start rates and a 56.43% drop in wasted memory time, compared to state-of-the-art methods (Lee et al., 26 Mar 2024).

3.4 Provider-Side Dependency Image Migration

Targeting the high dependency initialization latency for functions with large software stacks, approaches such as WarmSwap manage a shared, in-memory pool of pre-initialized dependency images (using CRIU-based process checkpointing and lazy/bulk memory page transfer). On cold-start, containers rapidly restore dependencies via migration from the pool, bypassing the need for redundant dependency setup. Evaluations demonstrate 2.2×–3.2× speed-ups in dependency loading, with an 88% reduction in cache space relative to function-specific pre-warming, especially benefiting the long tail of infrequently used functions (Li et al., 13 Sep 2024).

3.5 Architectural Observations and Multi-Region Scheduling

Trace analysis exposes that cold-start bottlenecks are multifactorial: pod allocation, code/dependency deployment, and orchestration overheads vary by trigger type, runtime language, and requested resources. Statistical modeling (e.g., log-normal fits for cold start time) and utility ratio metrics offer actionable diagnostics. Regional differences advocate for multi-region scheduling strategies, where workloads may be shifted toward regions with lower cold-start and resource contention, further reducing latency and cost (Joosen et al., 8 Oct 2024).

4. Specialized and Emerging Cold-Start Domains

4.1 LLMs in Serverless Environments

For serverless LLM inference, cold-starts are dominated by massive model file fetching. ParaServe introduces pipeline parallelism, distributing the model load across multiple GPU servers, exploiting aggregate network/PCIe bandwidth. Model fetching, loading, and runtime initialization are overlapped via worker-level optimizations. Pipeline consolidation merges parallel workers to minimize warm request latency. This design achieves up to 4.7× reduction in cold-start and a 1.74× gain in SLO attainment relative to serial fetching baselines (Lou et al., 21 Feb 2025).

4.2 Item Cold-Start via Prompt Tuning

Recent works adapt prompt learning methodologies from NLP for recommender systems’ cold-start scenarios. Instead of content feature propagation, PROMO employs high-value "pinnacle feedback"—interactions reflecting the most engaged users—as item prompts. Item-wise personalized prompt networks further decouple model bias from popularity dominance. This yields superior offline metrics (HitRate@K, NDCG@K) and real-world engagement improvements (up to +4.8% in video play time) on a billion-scale commercial short-video platform (Jiang et al., 24 Dec 2024).

4.3 Bundle Recommendation and Diffusion Modeling

In cold-start bundle recommendation, the challenge is increased by multi-level, multi-view data structure: new bundles lack history at both the bundle and constituent item level. The MoDiffE framework addresses this with a divide-and-conquer approach—segmenting the cold-start into sub-problems by level and view, then generating representations for feature-missing bundles/items via diffusion models, and fusing the results through a cold-aware hierarchical mixture-of-experts gating network. Experiments report up to 0.1027 gain in Recall@20 for cold-start bundles and 47.43% relative improvement overall (Li et al., 8 May 2025).

4.4 End-User Driven Preference Transfer

Pretender algorithm empowers end users to address the cold-start problem in new platforms by transferring their own historical preferences—without provider assistance. Formulated as a discrepancy minimization between empirical source and target preference distributions (using IPM metrics such as MMD or Wasserstein distance), the procedure relies on continuous convex optimization and randomized rounding to select a portfolio of $K$ target items. Theoretical results guarantee $O(1/\sqrt{K})$ error convergence with respect to the combinatorially optimal value. Empirical tests on MovieLens, Last.fm, and Amazon datasets show near-optimal regret and superior user experience compared to random or greedy selection (Sato, 18 Feb 2025).

5. Cold-Start in Active Data Acquisition and Preference Learning

Active learning strategies originally rely on classifier uncertainty to drive querying, but in the absence of any labeled data (cold-start), alternative selection metrics are required. One approach proxies informativeness by the loss from a self-supervised pre-trained LLM: masked LLMing (MLM) loss from BERT is used to rank unlabeled samples, leading to more rapid accuracy gains and fewer annotation rounds than traditional procedures (Yuan et al., 2020). In active preference learning for socio-economic systems, cold-start is addressed with a self-supervised PCA-based pseudo-labeling phase, generating pairwise preference labels from principal component projections, which "warms up" an XGBoost-based classifier before the active acquisition loop. This hybrid increases sample efficiency and outperforms random or uncertainty sampling baselines (Fayaz-Bakhsh et al., 7 Aug 2025).

6. Open Challenges and Future Research Directions

Fundamental questions remain regarding the generalizability of cold-start strategies across algorithmic paradigms and their optimal integration with side-information, cross-domain knowledge, and adaptive resource allocation. Notable problems include the extension of targeting and pseudo-labelling methods to algorithms beyond ICF (e.g., neural networks, content-based and hybrid models), more sophisticated combinatorial optimization for end-user transfer, orchestrated multi-region resource scheduling, and adaptive pooling for highly transient or bursty workloads. Cost–benefit analysis, domain adaptation, and explicit handling of popularity and semantic gaps are critical for translating research results into practical, scalable deployments.

7. Summary Table: Cold-Start Strategies and Domains

Domain	Principal Cold-Start Strategy	Key Outcome/Metric
Recommender systems	Low-degree user targeting, meta-learning	Increased exposure; lower MAE
Serverless computing	Pool-based pre-warming, predictive autoscaling	Up to 85% P99 latency reduction
LLM serving (serverless)	Pipeline parallelism, worker-level optimization	4.7× lower TTFT; higher SLO success
Bundle/item recommendation	Diffusion modeling, mixture-of-experts	Up to 0.1027 gain in Recall@20
Active learning / preference	Self-supervised pseudo-labeling, MLM loss ranking	Higher accuracy, fewer queries
End-user cross-platform transfer	Preference distribution alignment (MMD/Wasserstein)	Fast regret decay, optimal transfer

This field continues to advance as research clarifies the algorithmic, architectural, and operational dimensions of the cold-start problem, yielding increasingly targeted and theoretically grounded strategies for a wide range of applications.