RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

• URL: http://arxiv.org/abs/2202.06129v1
• Date: Sat, 12 Feb 2022 19:27:56 GMT
• Title: RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
• Authors: Ruijie Wang, Zheng Li, Danqing Zhang, Qingyu Yin, Tong Zhao, Bing Yin, Tarek Abdelzaher
• Abstract summary: Temporal event forecasting is a new user behavior prediction task in a unified query product evolutionary graph. We propose a novel RetrievalEnhanced Event forecasting framework. Unlike existing methods, we propose methods that enhance user representations via roughly connected entities in the whole graph.
• Score: 18.826901341496143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions with search queries, which usually act as a bridge between users and products, are still under investigated. In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. To fulfill this setting, there involves two challenges: (1) the action data for most users is scarce; (2) user preferences are dynamically evolving and shifting over time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal Event (RETE) forecasting framework. Unlike existing methods that enhance user representations via roughly absorbing information from connected entities in the whole graph, RETE efficiently and dynamically retrieves relevant entities centrally on each user as high-quality subgraphs, preventing the noise propagation from the densely evolutionary graph structures that incorporate abundant search queries. And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction. Empirically, extensive experiments on both the public benchmark and four real-world industrial datasets demonstrate the effectiveness of the proposed RETE method.

Related papers

• 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)
• Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation [28.29435760797856]
  We propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N) -- for click-through rate prediction in TIR scenarios. We design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. We evaluate our method on several offline and real-world industrial datasets.
  arXiv  Detail & Related papers  (2024-01-15T15:27:24Z)
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction [15.97120392599086]
  We propose textbfM (textbfSampling-based textbfDeep textbfModeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. We show theoretically and experimentally that the proposed method performs on par with standard attention-based models on modeling long-term user behaviors.
  arXiv  Detail & Related papers  (2022-05-20T15:20:52Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [3.286961611175469]
  We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation. It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
  arXiv  Detail & Related papers  (2021-07-12T07:34:20Z)
• 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)
• Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction [48.267995749975476]
  In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest. Existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction. We propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features.
  arXiv  Detail & Related papers  (2020-12-13T05:46:19Z)

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.