Graph-Based Retrieval for Promoted Jobs

Updated 26 December 2025

The paper introduces graph construction paradigms that integrate heterogeneous node types and promotion edges to improve retrieval accuracy.
Graph-based retrieval employs link-based models, GNN embeddings, and LLM meta-path prompting to capture multi-hop relational semantics effectively.
Empirical evaluations show significant gains in offline and online metrics, boosting exposure, budget efficiency, and handling cold-start scenarios.

Graph-based retrieval for promoted jobs refers to a set of methodologies in large-scale recruitment and job matching platforms that represent candidate, job, and behavioral data as graphs, and utilize various graph mining or embedding algorithms to enhance the retrieval and ranking of promoted or sponsored job postings. The central aim is to leverage graph structure—encompassing user–job interactions, attribute affinities, and engagement signals—to improve the exposure, matching accuracy, and effectiveness of promoted jobs, even under data sparsity or cold-start constraints. Recent advancements integrate heterogeneous graphs, graph neural networks (GNNs), and LLMs to encode not only direct user–job signals but also complex multi-hop relational semantics and dynamically injected promotion information.

1. Graph Construction Paradigms for Promoted Job Retrieval

The construction of the underlying graph is a foundational step, dictating the scope of relational knowledge that can be exploited for retrieval and ranking. Systems vary in their node, edge, and feature formalism:

Heterogeneous Node Types: Job-seeker/member nodes, job-posting nodes (organic and promoted), skill, title, company, and “segment” nodes representing attribute conjunctions (Liu et al., 20 Feb 2024, Shen et al., 21 Feb 2024).
Edge Semantics: Directed behavioral (view, click, apply, interview, message), skill-ownership, relational (e.g., member↔skill, member↔title), and special “promotion” or “sponsored” edges created by publisher-driven incentives (Liu et al., 20 Feb 2024, Wu et al., 2023).
Feature Attribution: Node features include profile/resume text, job descriptions, DNN embeddings, recency-based stats, industry or seniority indicators; edges encode interaction types, recency, and promotion markers (Liu et al., 20 Feb 2024, Wu et al., 2023, Shen et al., 21 Feb 2024).

For large-scale platforms, storage adopts distributed adjacency-list or key-value stores, and sparse or dense matrix representations are materialized for query-time efficiency (Shalaby et al., 2018, Liu et al., 20 Feb 2024, Shen et al., 21 Feb 2024).

2. Retrieval Models and Learning Objectives

Three principal retrieval model types address promoted job selection:

Link-based Retrieval (Learning-to-Retrieve): The job-seeker/job graph is augmented with one or more segment/link layers, capturing high-quality attribute conjunctions between seeker and job segments. Each such “complex link” is assigned a scalar quality score, $q_C$ , trained via ℓ₁-regularized logistic regression on confirmed hire records. This explicitly reflects $P(\text{hire}|C)$ and enables highly interpretable, sparse retrieval (Shen et al., 21 Feb 2024).
Graph Embedding with GNNs: An encoder–decoder GNN (e.g., heterogeneous GraphSAGE) computes member ( $h_u$ ) and job ( $h_j$ ) embeddings integrating K-hop multi-type neighborhood signals. The model is trained on historical engagement via dot-product or MLP decoders, with task-specific losses (cross-entropy, contrastive) for link prediction. After encoder pretraining, embeddings are injected into downstream DNN rankers (Liu et al., 20 Feb 2024).
Meta-Path LLM Prompting: The GLRec framework encodes higher-order behavior via meta-path-based prompt constructors. Sampled meta-paths (sequences of types and edges) are transformed into concatenated natural-language prompts and input alongside candidate/job text into a fine-tuned LLM. Augmentation modules (shuffling, soft-selection) mitigate prompt bias and encode path importance (Wu et al., 2023).

The loss functions range from cross-entropy (with negative sampling) in GNNs and link regressions to contrastive losses (embedding discrimination) and autoregressive generation objectives (LLMs) (Wu et al., 2023, Shen et al., 21 Feb 2024, Liu et al., 20 Feb 2024).

3. Serving, Inference, and Latency Considerations

Production retrieval for promoted jobs operates under strict latency constraints (≤10–20 ms typical). Efficient serving is achieved through:

Nearline Embedding Computation: For GNN-based systems, embeddings are precomputed in response to graph updates (e.g., new job postings, promotion injection) using batch inference pipelines. Embeddings are stored in feature stores (Redis, DeepGNN) keyed by node ID (Liu et al., 20 Feb 2024).
Indexing and Lookup: Retrieval comprises fast lookups of seeker/job embeddings and scoring via dot products, optionally incorporating learned promotion bias terms ( $b_j$ for jobs, which may encode promotion spend or recency) (Liu et al., 20 Feb 2024).
On-GPU Candidate Selection: Combined Boolean (term-based) and dense KNN retrieval is implemented on GPU, fusing attribute-matching and embedding-based similarity into a unified top-K retrieval kernel (Shen et al., 21 Feb 2024).

Pseudo-code for typical retrieval logic:

def retrieve_promoted_jobs(member_id, candidate_jobs, topK=10):
    h_u = feature_store.lookup("mem_emb", member_id)
    scores = []
    for job_id in candidate_jobs:
        h_j = feature_store.lookup("job_emb", job_id)
        b_prom = promoted_bias.get(job_id, 0.0)
        s = dot(h_u, h_j) + b_prom
        scores.append((job_id, s))
    return sorted(scores, key=lambda x: x[1], reverse=True)[:topK]

(Liu et al., 20 Feb 2024)

4. Cold-Start, Out-of-Distribution, and Promotion Mechanisms

A major innovation in graph-based promoted job retrieval is the capacity to recommend and elevate jobs that are new (cold-start) or otherwise out-of-distribution (OOD):

Immediate Graph Integration: For newly published or promoted jobs, edge creation is supported by content embeddings (e.g., neural similarity), enabling rapid seeding of the job in the retrieval graph without waiting for behavioral signals (Shalaby et al., 2018).
Meta-Path Injection for OOD Promotion: The GLRec framework enables LLMs to extrapolate candidate preferences to previously unseen job types by leveraging rich meta-path semantics and external language knowledge. Sponsored meta-paths (e.g., a recruiter introducing a candidate to a promoted job) allow explicit integration of promotion events, mitigating the cold-start effect (Wu et al., 2023).
Link Reweighting for Budget Allocation: Learned link quality scores, $q_C$ , enable liquidity control and rationalization of exposure such that qualified matches are prioritized without over-serving generic candidates, supporting recruiter budget utilization (Shen et al., 21 Feb 2024).

5. Evaluations and Observed Impact

Empirical studies demonstrate consistent uplift and scalability from graph-based promoted job retrieval:

System/Paper	Offline Metric	Online Metric/Impact
GLRec (Wu et al., 2023)	AUC up to 0.891 (random), 0.81 (OOD)	Absolute AUC gains 13–30% vs. baselines (OOT scenarios)
LinkSAGE (Liu et al., 20 Feb 2024)	A/B Promoted CTR +1.8%	Apply Clicks +0.4%; Successful Sessions +1.1%
GBR (Shalaby et al., 2018)	≈90% expert-judged relevancy	EOI per open ≈23% (vs 11% for matrix-factorization baseline)
Learnt Link Graph (Shen et al., 21 Feb 2024)	9K–70K links at target recall	+15% budget utilization over atomic-attribute baseline

Graph-based models consistently outperform pure content or collaborative-filtering approaches in both offline and production metrics, and are particularly robust in cold-start, OOD, and promoted job scenarios (Wu et al., 2023, Liu et al., 20 Feb 2024, Shalaby et al., 2018, Shen et al., 21 Feb 2024).

6. Explainability, Manual Control, and Ecosystem Integration

A distinguishing property of graph-based promoted job retrieval systems, particularly those employing explicit link graphs, is their explainability:

Human-Readable Links: Each retrieved seeker–job pair can be traced to attribute conjunctions and their weights, supporting intuitive debugging and manual rule overrides (e.g., disabling links deemed undesirable or boosting underrepresented recruiter needs) (Shen et al., 21 Feb 2024).
Transparent Budget and Exposure Control: The learned weights $q_C$ allow precise control of promotion pool liquidity per job or seeker, balancing reach and quality at fine granularity (Shen et al., 21 Feb 2024).
Composability with Staged Retrieval-and-Ranking Pipelines: Embedding- and link-based retrieval layers are natively compatible with downstream DNN rankers and auction logic, enabling end-to-end optimization under realistic latency and scale constraints (Liu et al., 20 Feb 2024, Shen et al., 21 Feb 2024, Shalaby et al., 2018).

7. Methodological Advances and Future Directions

The current literature reflects a progression from pure behavioral (collaborative filtering) signals (Shalaby et al., 2018), through hybrid GNNs integrating multi-hop attribute and interaction structure (Liu et al., 20 Feb 2024), to LLM-based meta-path prompting pipelines that encode high-order semantic behavior and support OOD extrapolation (Wu et al., 2023). A plausible implication is continued advancement toward unified architectures that combine explicit graph mining, self-supervised embedding pre-training, LLM prompt engineering, and learning-to-retrieve techniques, facilitating increased robustness, explainability, and operational agility in promoted job matching at industrial web scale.