Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

• URL: http://arxiv.org/abs/2305.05848v1
• Date: Wed, 10 May 2023 02:42:12 GMT
• Title: Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
• Authors: Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan
• Abstract summary: 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.
• Score: 74.81561396321712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNN

Related papers

• Learning Social Graph for Inactive User Recommendation [50.090904659803854]
  LSIR learns an optimal social graph structure for social recommendation, especially for inactive users. Experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation.
  arXiv  Detail & Related papers  (2024-05-08T03:40:36Z)
• Knowledge-Enhanced Recommendation with User-Centric Subgraph Network [38.814514460928386]
  We propose Knowledge-enhanced User-Centric subgraph Network (KUCNet) for effective recommendation. KUCNet is a subgraph learning approach with graph neural network (GNN) for effective recommendation. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items.
  arXiv  Detail & Related papers  (2024-03-21T13:09:23Z)
• Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems [4.026354668375411]
  We propose a novel reinforcement learning-based message passing framework for recommender systems. This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Our results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively.
  arXiv  Detail & Related papers  (2023-02-21T09:47:27Z)
• Graph Spring Network and Informative Anchor Selection for Session-based Recommendation [2.6524289609910654]
  Session-based recommendation (SBR) aims at predicting the next item for an ongoing anonymous session. The major challenge of SBR is how to capture richer relations in between items and learn ID-based item embeddings to capture such relations. We propose a new graph neural network, called Graph Spring Network (GSN), for learning ID-based item embedding on an item graph.
  arXiv  Detail & Related papers  (2022-02-19T02:47:44Z)
• Federated Social Recommendation with Graph Neural Network [69.36135187771929]
  We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
  arXiv  Detail & Related papers  (2021-11-21T09:41:39Z)
• LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking [62.634516517844496]
  We propose LNN-EL, a neuro-symbolic approach that combines the advantages of using interpretable rules with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches.
  arXiv  Detail & Related papers  (2021-06-17T20:22:45Z)
• Counterfactual Explanations for Neural Recommenders [10.880181451789266]
  We propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. We use ACCENT to generate counterfactual explanations for two popular neural models.
  arXiv  Detail & Related papers  (2021-05-11T13:16:18Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks [59.378148590027735]
  We propose a graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores.
  arXiv  Detail & Related papers  (2021-01-06T03:05:37Z)

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.