Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation

• URL: http://arxiv.org/abs/2410.10296v1
• Date: Mon, 14 Oct 2024 08:49:11 GMT
• Title: Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
• Authors: Xinping Zhao, Chaochao Chen, Jiajie Su, Yizhao Zhang, Baotian Hu,
• Abstract summary: Session-based Recommendation (SBR) seeks to predict a user's next action based on an anonymous session. Most SBR models rely on the contextual transitions within a short session to learn item representations. We propose a model-agnostic framework, named AttrGAU, to bring the Modeling of Item Attributes's superiority into existing attribute-agnostic models.
• Score: 18.318271141864297
• License:
• Abstract: Session-based Recommendation (SBR), seeking to predict a user's next action based on an anonymous session, has drawn increasing attention for its practicability. Most SBR models only rely on the contextual transitions within a short session to learn item representations while neglecting additional valuable knowledge. As such, their model capacity is largely limited by the data sparsity issue caused by short sessions. A few studies have exploited the Modeling of Item Attributes (MIA) to enrich item representations. However, they usually involve specific model designs that can hardly transfer to existing attribute-agnostic SBR models and thus lack universality. In this paper, we propose a model-agnostic framework, named AttrGAU (Attributed Graph Networks with Alignment and Uniformity Constraints), to bring the MIA's superiority into existing attribute-agnostic models, to improve their accuracy and robustness for recommendation. Specifically, we first build a bipartite attributed graph and design an attribute-aware graph convolution to exploit the rich attribute semantics hidden in the heterogeneous item-attribute relationship. We then decouple existing attribute-agnostic SBR models into the graph neural network and attention readout sub-modules to satisfy the non-intrusive requirement. Lastly, we design two representation constraints, i.e., alignment and uniformity, to optimize distribution discrepancy in representation between the attribute semantics and collaborative semantics. Extensive experiments on three public benchmark datasets demonstrate that the proposed AttrGAU framework can significantly enhance backbone models' recommendation performance and robustness against data sparsity and data noise issues. Our implementation codes will be available at https://github.com/ItsukiFujii/AttrGAU.

Related papers

• Spectral-Based Graph Neural Networks for Complementary Item Recommendation [37.25756903883821]
  We present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) We make the first observation that complementary relationships consist of low-frequency and mid-frequency components. We propose a two-stage attention mechanism to adaptively integrate and balance the two attributes.
  arXiv  Detail & Related papers  (2024-01-04T08:31:47Z)
• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification [78.52704557647438]
  We propose a novel FIne-grained Representation and Recomposition (FIRe$2$) framework to tackle both limitations without any auxiliary annotation or data. Experiments demonstrate that FIRe$2$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
  arXiv  Detail & Related papers  (2023-08-21T12:59:48Z)
• Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning [52.506434446439776]
  Compositional zero-shot learning (CZSL) aims to recognize compositions with prior knowledge of known primitives (attribute and object) We propose a simple and scalable framework called Composition Transformer (CoT) to address these issues. Our method achieves SoTA performance on several benchmarks, including MIT-States, C-GQA, and VAW-CZSL.
  arXiv  Detail & Related papers  (2023-08-08T03:24:21Z)
• Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation [58.748215444851226]
  Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. We propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) to build a session-based recommendation model considering temporal information.
  arXiv  Detail & Related papers  (2023-01-10T04:16:09Z)
• A Graph-Enhanced Click Model for Web Search [67.27218481132185]
  We propose a novel graph-enhanced click model (GraphCM) for web search. We exploit both intra-session and inter-session information for the sparsity and cold-start problems.
  arXiv  Detail & Related papers  (2022-06-17T08:32:43Z)
• A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention [16.283718738518534]
  We propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information. ACAM is built with a co-attention mechanism on attribute-level to achieve performance gains. Our experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models.
  arXiv  Detail & Related papers  (2020-06-18T01:53:39Z)
• Attention improves concentration when learning node embeddings [1.2233362977312945]
  Given nodes labelled with search query text, we want to predict links to related queries that share products. Experiments with a range of deep neural architectures show that simple feedforward networks with an attention mechanism perform best for learning embeddings. We propose an analytically tractable model of query generation, AttEST, that views both products and the query text as vectors embedded in a latent space.
  arXiv  Detail & Related papers  (2020-06-11T21:21:12Z)
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach [61.2786065744784]
  In recommender systems, users and items are associated with attributes, and users show preferences to items. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
  arXiv  Detail & Related papers  (2020-05-25T10:50:01Z)
• SEEK: Segmented Embedding of Knowledge Graphs [77.5307592941209]
  We propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations.
  arXiv  Detail & Related papers  (2020-05-02T15:15:50Z)

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.