EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration

• URL: http://arxiv.org/abs/2406.14017v2
• Date: Wed, 03 Jul 2024 10:00:26 GMT
• Title: EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration
• Authors: Ye Wang, Jiahao Xun, Minjie Hong, Jieming Zhu, Tao Jin, Wang Lin, Haoyuan Li, Linjun Li, Yan Xia, Zhou Zhao, Zhenhua Dong,
• Abstract summary: We introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.
• Score: 63.112790050749695
• License:
• Abstract: Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either behavioral or semantic aspects of item information, neglecting their complementary nature and thus resulting in limited effectiveness. To address this limitation, we introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. Specifically, we identify three key challenges in combining these two types of information: a unified generative architecture capable of handling two feature types, ensuring sufficient and independent learning for each type, and fostering subtle interactions that enhance collaborative information utilization. To achieve these goals, we propose (1) a two-stream generation architecture leveraging a shared encoder and two separate decoders to decode behavior tokens and semantic tokens with a confidence-based ranking strategy; (2) a global contrastive task with summary tokens to achieve discriminative decoding for each type of information; and (3) a semantic-guided transfer task designed to implicitly promote cross-interactions through reconstruction and estimation objectives. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.

Related papers

• Generative Retrieval Meets Multi-Graded Relevance [104.75244721442756]
  We introduce a framework called GRaded Generative Retrieval (GR$2$) GR$2$ focuses on two key components: ensuring relevant and distinct identifiers, and implementing multi-graded constrained contrastive training. Experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of GR$2$.
  arXiv  Detail & Related papers  (2024-09-27T02:55:53Z)
• Learnable Item Tokenization for Generative Recommendation [78.30417863309061]
  We propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias.
  arXiv  Detail & Related papers  (2024-05-12T15:49:38Z)
• Feature Decoupling-Recycling Network for Fast Interactive Segmentation [79.22497777645806]
  Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input. We propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies.
  arXiv  Detail & Related papers  (2023-08-07T12:26:34Z)
• RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection [32.20132357830726]
  Language-Image Pre-training (LIPR) is a strategy for contrastive pre-training that leverages both entity and relation descriptions. We show the benefits of these contributions, collectively termed RLIP-ParSe, for improved zero-shot, few-shot and fine-tuning HOI detection as well as increased robustness from noisy annotations.
  arXiv  Detail & Related papers  (2022-09-05T07:50:54Z)
• CTRN: Class-Temporal Relational Network for Action Detection [7.616556723260849]
  We introduce an end-to-end network: Class-Temporal Network (CTRN) CTRN contains three key components: The Transform Representation Module, the Class-Temporal Module and the G-classifier. We evaluate CTR on three densely labelled datasets and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-10-26T08:15:47Z)
• Joint Inductive and Transductive Learning for Video Object Segmentation [107.32760625159301]
  Semi-supervised object segmentation is a task of segmenting the target object in a video sequence given only a mask in the first frame. Most previous best-performing methods adopt matching-based transductive reasoning or online inductive learning. We propose to integrate transductive and inductive learning into a unified framework to exploit complement between them for accurate and robust video object segmentation.
  arXiv  Detail & Related papers  (2021-08-08T16:25:48Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.