Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

• URL: http://arxiv.org/abs/2107.03813v1
• Date: Thu, 8 Jul 2021 12:38:26 GMT
• Title: Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
• Authors: Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long
• Abstract summary: 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.
• Score: 32.03389044375335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.

Related papers

• 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)
• 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)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• 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 Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• 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)
• 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.