SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network

• URL: http://arxiv.org/abs/2108.05641v3
• Date: Thu, 5 Oct 2023 08:28:44 GMT
• Title: SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
• Authors: Jinpeng Chen, Haiyang Li, Xudong Zhang, Fan Zhang, Senzhang Wang, Kaimin Wei and Jiaqi Ji
• Abstract summary: 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.
• Score: 20.82060191403763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, local and global session embeddings are combined with the attentional network to obtain the final session embedding, considering the influence of users' long and short-term preferences. 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.

Related papers

• Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning [49.262407095098645]
  Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. Users tend to adjust their job preferences to secure employment opportunities continually. We propose a novel session-based framework, BISTRO, to model user preference through fusion learning of semantic and behavioral information.
  arXiv  Detail & Related papers  (2024-06-24T14:38:04Z)
• Context-aware Session-based Recommendation with Graph Neural Networks [6.825493772727133]
  We propose CARES, a novel context-aware session-based recommendation model with graph neural networks. We first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. To encode the variation of user interests, we design personalized item representations.
  arXiv  Detail & Related papers  (2023-10-14T14:29:52Z)
• Learning Similarity among Users for Personalized Session-Based Recommendation from hierarchical structure of User-Session-Item [3.2872586139884623]
  We propose USP-SBR(abbr. of User Similarity Powered - Session Based Recommender) to model global historical sessions of users. We apply graph neural network on these UserGraph and update nodes. Experimental results on two real-world datasets demonstrate that our method outperforms some state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-05T17:03:10Z)
• Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation [74.81561396321712]
  We propose a dual-intent enhanced graph neural network for session-based recommendations. We learn user intent from an attention mechanism and the distribution of historical data. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users.
  arXiv  Detail & Related papers  (2023-05-10T02:42:12Z)
• Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints [0.0]
  Session-based recommendation attempts to recommend items by interpreting sessions that consist of sequences of items. In this paper, we consider various relationships in graph created by sessions through Heterogeneous attention network. It seeks to increase performance through additional optimization in the training process.
  arXiv  Detail & Related papers  (2022-05-23T14:35:26Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• G$^3$SR: Global Graph Guided Session-based Recommendation [116.38098186755029]
  Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation. G$3$SR (Global Graph Guided Session-based Recommendation) decomposes the session-based recommendation workflow into two steps. Experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G$3$SR method over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-12T15:44:03Z)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation [32.03389044375335]
  Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. We propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions. Based on the HGNN, we propose the Current Preference and the Historical Preference to capture different levels of user preference from the current and historical sessions.
  arXiv  Detail & Related papers  (2021-07-08T12:38:26Z)
• UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks [27.485553372163732]
  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.
  arXiv  Detail & Related papers  (2020-08-06T09:54:03Z)
• 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.