Time-aware Hyperbolic Graph Attention Network for Session-based
Recommendation
- URL: http://arxiv.org/abs/2301.03780v1
- Date: Tue, 10 Jan 2023 04:16:09 GMT
- Title: Time-aware Hyperbolic Graph Attention Network for Session-based
Recommendation
- Authors: Xiaohan Li, Yuqing Liu, Zheng Liu, Philip S. Yu
- Abstract summary: 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.
- Score: 58.748215444851226
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Session-based Recommendation (SBR) is to predict users' next interested items
based on their previous browsing sessions. Existing methods model sessions as
graphs or sequences to estimate user interests based on their interacted items
to make recommendations. In recent years, graph-based methods have achieved
outstanding performance on SBR. However, none of these methods consider
temporal information, which is a crucial feature in SBR as it indicates
timeliness or currency. Besides, the session graphs exhibit a hierarchical
structure and are demonstrated to be suitable in hyperbolic geometry. But few
papers design the models in hyperbolic spaces and this direction is still under
exploration. In this paper, we propose Time-aware Hyperbolic Graph Attention
Network (TA-HGAT) - a novel hyperbolic graph neural network framework to build
a session-based recommendation model considering temporal information. More
specifically, there are three components in TA-HGAT. First, a hyperbolic
projection module transforms the item features into hyperbolic space. Second,
the time-aware graph attention module models time intervals between items and
the users' current interests. Third, an evolutionary loss at the end of the
model provides an accurate prediction of the recommended item based on the
given timestamp. TA-HGAT is built in a hyperbolic space to learn the
hierarchical structure of session graphs. Experimental results show that the
proposed TA-HGAT has the best performance compared to ten baseline models on
two real-world datasets.
Related papers
- From random-walks to graph-sprints: a low-latency node embedding
framework on continuous-time dynamic graphs [4.372841335228306]
We propose a framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with state-of-the-art, higher latency models.
In our framework, time-aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges.
We demonstrate that our graph-sprints features, combined with a machine learning, achieve competitive performance.
arXiv Detail & Related papers (2023-07-17T12:25:52Z) - Temporal Graph Benchmark for Machine Learning on Temporal Graphs [54.52243310226456]
Temporal Graph Benchmark (TGB) is a collection of challenging and diverse benchmark datasets.
We benchmark each dataset and find that the performance of common models can vary drastically across datasets.
TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research.
arXiv Detail & Related papers (2023-07-03T13:58:20Z) - FTM: A Frame-level Timeline Modeling Method for Temporal Graph
Representation Learning [47.52733127616005]
We propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features.
Our method can be easily assembled with most temporal GNNs.
arXiv Detail & Related papers (2023-02-23T06:53:16Z) - 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) - Position-enhanced and Time-aware Graph Convolutional Network for
Sequential Recommendations [3.286961611175469]
We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN)
PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation.
It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
arXiv Detail & Related papers (2021-07-12T07:34:20Z) - HCGR: Hyperbolic Contrastive Graph Representation Learning for
Session-based Recommendation [5.942131706372327]
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.
We present a hyperbolic contrastive graph recommender (HCGR) to adequately capture the coherence and hierarchical representations of the items.
arXiv Detail & Related papers (2021-07-06T01:46:16Z) - From Static to Dynamic Node Embeddings [61.58641072424504]
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications.
Our proposed framework includes novel methods for learning an appropriate graph time-series representation.
We find that the top-3 temporal models are always those that leverage the new $epsilon$-graph time-series representation.
arXiv Detail & Related papers (2020-09-21T16:48:29Z) - 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.