UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks

• URL: http://arxiv.org/abs/2008.02546v1
• Date: Thu, 6 Aug 2020 09:54:03 GMT
• Title: UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
• Authors: Bo Huang, Ye Bi, Zhenyu Wu, Jianming Wang, Jing Xiao
• Abstract summary: We propose User-Based Embeddings Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes advantage of structured data to generate longterm user preferences, and transfers session sequences into graphs to generate graph-based dynamic interests. Experiments conducted on real Ping An scenario show that UBER-GNN outperforms the state-of-the-art session-based recommendation methods.
• Score: 27.485553372163732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user latent features by RNN and GNN methods to make recommendations. However under massive-scale and complicated financial recommendation scenarios with both virtual and real commodities , such methods are not sufficient to represent accurate user latent features and neglect the long-term characteristics of users. To take long-term preference and dynamic interests into account, we propose a novel method, i.e. User-Based Embeddings Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes advantage of structured data to generate longterm user preferences, and transfers session sequences into graphs to generate graph-based dynamic interests. The final user latent feature is then represented as the composition of the long-term preferences and the dynamic interests using attention mechanism. Extensive experiments conducted on real Ping An scenario show that UBER-GNN outperforms the state-of-the-art session-based recommendation methods.

Related papers

• Linear-Time Graph Neural Networks for Scalable Recommendations [50.45612795600707]
  The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. We propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches.
  arXiv  Detail & Related papers  (2024-02-21T17:58:10Z)
• Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Periods [5.369499761777157]
  This research presents a novel method that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph. The model is applied to real-world data to argue its superior performance.
  arXiv  Detail & Related papers  (2023-12-21T21:36:43Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network [20.82060191403763]
  Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. We propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.
  arXiv  Detail & Related papers  (2021-08-12T10:12:48Z)
• Denoising User-aware Memory Network for Recommendation [11.145186013006375]
  We propose a novel CTR model named denoising user-aware memory network (DUMN) DUMN uses the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback. Experiments on two real e-commerce user behavior datasets show that DUMN has a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-07-12T14:39:36Z)
• Context-aware short-term interest first model for session-based recommendation [0.0]
  We propose a context-aware short-term interest first model (CASIF) The aim of this paper is improve the accuracy of recommendations by combining context and short-term interest. In the end, the short-term and long-term interest are combined as the final interest and multiplied by the candidate vector to obtain the recommendation probability.
  arXiv  Detail & Related papers  (2021-03-29T11:36:00Z)
• Dynamic Memory based Attention Network for Sequential Recommendation [79.5901228623551]
  We propose a novel long sequential recommendation model called Dynamic Memory-based Attention Network (DMAN) It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users. Based on the dynamic memory, the user's short-term and long-term interests can be explicitly extracted and combined for efficient joint recommendation.
  arXiv  Detail & Related papers  (2021-02-18T11:08:54Z)
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [66.04457457299218]
  We propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
  arXiv  Detail & Related papers  (2020-05-06T14:17:05Z)

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.