Neural Hierarchical Factorization Machines for User's Event Sequence Analysis

• URL: http://arxiv.org/abs/2112.15292v1
• Date: Fri, 31 Dec 2021 04:08:55 GMT
• Title: Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
• Authors: Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He
• Abstract summary: We consider a two-level structure for capturing the hierarchical information over user's event sequence. Our model achieves significantly better performance compared with state-of-the-art baselines.
• Score: 21.13650689194003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular solutions usually suffer two key issues: 1) only focusing on feature interactions and failing to capture the sequence influence; 2) only focusing on sequence information, but ignoring internal feature relations of each event, thus failing to extract a better event representation. In this paper, we consider a two-level structure for capturing the hierarchical information over user's event sequence: 1) learning effective feature interactions based event representation; 2) modeling the sequence representation of user's historical events. Experimental results on both industrial and public datasets clearly demonstrate that our model achieves significantly better performance compared with state-of-the-art baselines.

Related papers

• Looking around you: external information enhances representations for event sequences [2.1879059908547482]
  We propose a method that aggregates information from multiple user representations augmenting a specific user one. Our study considers diverse aggregation approaches, ranging from simple pooling techniques to trainable attention-based approaches. The proposed method operates atop an existing encoder and supports its efficient fine-tuning.
  arXiv  Detail & Related papers  (2025-02-14T14:59:37Z)
• 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. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Distilling Event Sequence Knowledge From Large Language Models [17.105913216452738]
  Event sequence models have been found to be highly effective in the analysis and prediction of events. We use Large Language Models to generate event sequences that can effectively be used for probabilistic event model construction. We show that our approach can generate high-quality event sequences, filling a knowledge gap in the input KG.
  arXiv  Detail & Related papers  (2024-01-14T09:34:42Z)
• Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction [15.02043375212839]
  This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score.
  arXiv  Detail & Related papers  (2023-06-30T15:22:57Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction [16.87868728956481]
  We propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE) To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.
  arXiv  Detail & Related papers  (2022-06-07T15:11:42Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker [23.990907956996413]
  Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task. We propose Heterogeneous Graph-based Interaction Model with a Tracker.
  arXiv  Detail & Related papers  (2021-05-31T12:45:03Z)
• 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)
• Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning [63.91369308085091]
  We propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set.
  arXiv  Detail & Related papers  (2020-06-14T13:21:37Z)

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.