Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation

• URL: http://arxiv.org/abs/2507.23209v1
• Date: Thu, 31 Jul 2025 03:05:05 GMT
• Title: Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
• Abstract summary: Time intervals between purchasing items are a crucial factor in sequential recommendation tasks.<n>We propose IntervalLLM, a framework that integrates interval information into LLM.<n>Our experiments show that IntervalLLM achieves 4.4% improvements in average but also the best-performing warm and cold scenarios.
• Score: 4.929399529593514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time intervals between purchasing items are a crucial factor in sequential recommendation tasks, whereas existing approaches focus on item sequences and often overlook by assuming the intervals between items are static. However, dynamic intervals serve as a dimension that describes user profiling on not only the history within a user but also different users with the same item history. In this work, we propose IntervalLLM, a novel framework that integrates interval information into LLM and incorporates the novel interval-infused attention to jointly consider information of items and intervals. Furthermore, unlike prior studies that address the cold-start scenario only from the perspectives of users and items, we introduce a new viewpoint: the interval perspective to serve as an additional metric for evaluating recommendation methods on the warm and cold scenarios. Extensive experiments on 3 benchmarks with both traditional- and LLM-based baselines demonstrate that our IntervalLLM achieves not only 4.4% improvements in average but also the best-performing warm and cold scenarios across all users, items, and the proposed interval perspectives. In addition, we observe that the cold scenario from the interval perspective experiences the most significant performance drop among all recommendation methods. This finding underscores the necessity of further research on interval-based cold challenges and our integration of interval information in the realm of sequential recommendation tasks. Our code is available here: https://github.com/sony/ds-research-code/tree/master/recsys25-IntervalLLM.

Related papers

• SwapRec: Warming Up Cold Items Through Training-Time Swaps [0.6716339721155583]
  Cold items negatively impact real-time personalization of ID-based recommender systems.<n>This is because the use of such interactions degrades user preference estimates.<n>One often applied to address this shortcoming at inference time is to replace, i.e., "swap", cold-start items by their most similar "warm" neighbor.
  arXiv  Detail & Related papers  (2026-09-01T08:41:58Z)
• Evidence-Driven Dynamic Visual Selector for Efficient Long Video Understanding [54.840531495584095]
  We propose a dynamic visual selection framework named EviSelect grounded in the target MLLM's internal attention evidence.<n>Our method efficiently approximates maps of the target MLLM using highly compressed visual inputs and sparse attention.<n>EviSelect achieves superior performance compared to existing methods while reducing selected visual tokens by about 50% and achieving a 3.9x end-to-end speedup.
  arXiv  Detail & Related papers  (2026-08-06T09:15:43Z)
• TRUST: Item-Calibrated Interval Evidence for Temporal Session-Based Recommendation [9.041970067606773]
  We propose a framework that evaluates each observed interval relative to the empirical interval distribution of the corresponding item.<n>Experiments show that the proposed scoring function can improve existing temporal session recommenders as a model-agnostic method.
  arXiv  Detail & Related papers  (2026-06-25T16:05:36Z)
• HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation [24.720767926024433]
  HyMiRec is a hybrid sequential recommendation framework for large language models.<n>It extracts coarse interest embeddings from long user sequences and an LLM-based recommender to captures refined interest embeddings.<n>To model the diverse preferences of users, we design a disentangled multi-interest learning module.
  arXiv  Detail & Related papers  (2025-10-15T16:45:59Z)
• SS4Rec: Continuous-Time Sequential Recommendation with State Space Models [2.736436208785024]
  We propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation.<n> SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies.<n>This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations.
  arXiv  Detail & Related papers  (2025-02-12T05:28:08Z)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms.<n>Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction.<n>We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• SA-LSPL:Sequence-Aware Long- and Short- Term Preference Learning for next POI recommendation [19.40796508546581]
  Point of Interest (POI) recommendation aims to recommend the POI for users at a specific time. We propose a novel approach called Sequence-Aware Long- and Short-Term Preference Learning (SA-LSPL) for next-POI recommendation.
  arXiv  Detail & Related papers  (2024-03-30T13:40:25Z)
• Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers [55.46413719810273]
  rich-temporal information is crucial to the complicated target appearance in visual tracking. Our method improves the tracker's performance on six popular tracking benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T02:39:26Z)
• Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention [3.7182810519704095]
  Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Recent models often neglect similarities in users' actions that occur implicitly among users during analogous timeframes. We propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities.
  arXiv  Detail & Related papers  (2024-02-15T08:33:16Z)
• Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation [62.997667081978825]
  We introduce a new approach named FARM - Forward Relevance Aligned Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series. As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
  arXiv  Detail & Related papers  (2023-04-21T15:22:33Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation [5.416421678129053]
  This paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)" The performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-16T09:12:33Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)
• Dynamic Embeddings for Interaction Prediction [2.5758502140236024]
  In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. Recent studies have shown the effectiveness of modeling the mutual interactions between users and items using separate user and item embeddings. We propose a novel method called DeePRed that addresses some of their limitations.
  arXiv  Detail & Related papers  (2020-11-10T16:04:46Z)

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.