Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs

• URL: http://arxiv.org/abs/2609.15692v1
• Date: Mon, 14 Sep 2026 14:59:08 GMT
• Title: Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs
• Abstract summary: We propose a graphbased metric-learning framework that reformulates complete suffix prediction as latent retrieval over process graphs.<n>We show that graph-based latent retrieval is an effective alternative to sequential suffix prediction for recommendation-oriented process monitoring.
• Score: 0.06524460254566904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complete suffix prediction is challenging in sequential decision settings, where the same prefix can remain compatible with several plausible suffixes. We propose a graphbased metric-learning framework that reformulates complete suffix prediction as latent retrieval over process graphs. Prefixes and suffixes are represented as directed attributed graphs and encoded by edge-conditioned graph neural networks, allowing event-level activities and transition-level durations to be modelled jointly. Prefix representations are projected into the latent suffix space through a predictor trained with a joint reconstruction and contrastive objective strengthened using process-aware hard negatives. To stabilise the learned retrieval geometry, spectral normalisation, and retrieval robustness, spectral normalisation is applied to enforce a Lipschitz constraint on both encoders and predictor. Experiments on two real-life process datasets demonstrate that the proposed framework achieves the best overall results across nearly all evaluated criteria. It improves semantic suffix accuracy measured by normalized Damerau-Levenshtein distance, yields strong retrieval quality through Recall@1, Recall@5, and MRR@5, and maintains temporal plausibility according to Mean Absolute Error. These results show that graph-based latent retrieval is an effective alternative to sequential suffix prediction for recommendation-oriented process monitoring under structural and KPI-related constraints.

Related papers

• REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation [64.84437332310516]
  We introduce textbfREER-PT, a scalable framework that extends Reverse-Engineered Reasoning (REER) to raw pre-training data.<n>REER-PT identifies continuations that are difficult to predict but can still be inferred from the preceding context, and inserts concise reasoning annotations that reconstruct the missing connection between context and continuation.<n>This sparse transformation preserves the source text and remains compatible with standard next-token prediction, avoiding online reasoning rollouts during pre-training.
  arXiv  Detail & Related papers  (2026-08-31T11:34:19Z)
• Progressive Cramming: Reliable Token Compression and What It Reveals [5.7789316833104225]
  Token cramming compresses sequences into learned embeddings with near-perfect reconstruction.<n>We introduce progressive cramming, which grows the target prefix token-by-token, stopping only when reconstruction is no longer achievable.
  arXiv  Detail & Related papers  (2026-07-23T11:46:36Z)
• Doubly Robust Proxy Causal Learning with Neural Mean Embeddings [58.8808794752741]
  We develop a neural doubly robust framework for proxy causal learning with continuous and structured treatments.<n>The framework covers population, heterogeneous, and conditional dose-response functions, yielding full response-curve estimators.<n>Across synthetic and image-valued benchmarks, the proposed estimators outperform existing baselines and single-bridge neural estimators.
  arXiv  Detail & Related papers  (2026-05-10T12:50:25Z)
• DANCE: Doubly Adaptive Neighborhood Conformal Estimation [12.643121779828526]
  We propose a doubly locally adaptive nearest-neighbor based conformal algorithm combining two novel nonconformity scores directly using the data's embedded representation.<n>We test against state-of-the-art local, task-adapted and zero-shot conformal baselines, demonstrating DANCE's superior blend of set size efficiency and robustness across various datasets.
  arXiv  Detail & Related papers  (2026-02-24T07:54:53Z)
• The Coverage Principle: How Pre-Training Enables Post-Training [70.25788947586297]
  We study how pre-training shapes the success of the final model.<n>We uncover a mechanism that explains the power of coverage in predicting downstream performance.
  arXiv  Detail & Related papers  (2025-10-16T17:53:50Z)
• Aligning the Evaluation of Probabilistic Predictions with Downstream Value [2.6636053598505307]
  Metrics based solely on predictive performance often diverge from measures of real-world downstream impact.<n>We propose a data-driven method to learn a proxy evaluation function aligned with the downstream evaluation.<n>Our approach leverages weighted scoring rules parametrized by a neural network, where weighting is learned to align with the performance in the downstream task.
  arXiv  Detail & Related papers  (2025-08-25T17:41:27Z)
• Relational Conformal Prediction for Correlated Time Series [56.59852921638328]
  We address the problem of uncertainty quantification in time series by exploiting correlated sequences.<n>We propose a novel distribution-free approach based on conformal prediction framework and quantile regression.<n>Our approach provides accurate coverage and achieves state-of-the-art uncertainty quantification in relevant benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T16:12:17Z)
• Variational Inference with Coverage Guarantees in Simulation-Based Inference [18.818573945984873]
  We propose Conformalized Amortized Neural Variational Inference (CANVI) CANVI constructs conformalized predictors based on each candidate, compares the predictors using a metric known as predictive efficiency, and returns the most efficient predictor. We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.
  arXiv  Detail & Related papers  (2023-05-23T17:24:04Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Learning Prediction Intervals for Regression: Generalization and Calibration [12.576284277353606]
  We study the generation of prediction intervals in regression for uncertainty quantification. We use a general learning theory to characterize the optimality-feasibility tradeoff that encompasses Lipschitz continuity and VC-subgraph classes. We empirically demonstrate the strengths of our interval generation and calibration algorithms in terms of testing performances compared to existing benchmarks.
  arXiv  Detail & Related papers  (2021-02-26T17:55:30Z)
• Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading [96.48553941812366]
  Lip-reading aims to infer the speech content from the lip movement sequence. Traditional learning process of seq2seq models suffers from two problems. We propose a novel pseudo-convolutional policy gradient (PCPG) based method to address these two problems.
  arXiv  Detail & Related papers  (2020-03-09T09:12:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.