138 research outputs found
Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict user actions
based on anonymous sessions. Previous methods model a session as a sequence and
estimate user representations besides item representations to make
recommendations. Though achieved promising results, they are insufficient to
obtain accurate user vectors in sessions and neglect complex transitions of
items. To obtain accurate item embedding and take complex transitions of items
into account, we propose a novel method, i.e. Session-based Recommendation with
Graph Neural Networks, SR-GNN for brevity. In the proposed method, session
sequences are modeled as graph-structured data. Based on the session graph, GNN
can capture complex transitions of items, which are difficult to be revealed by
previous conventional sequential methods. Each session is then represented as
the composition of the global preference and the current interest of that
session using an attention network. Extensive experiments conducted on two real
datasets show that SR-GNN evidently outperforms the state-of-the-art
session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial
Intelligence (AAAI-19
Rethinking Item Importance in Session-based Recommendation
Session-based recommendation aims to predict users' based on anonymous
sessions. Previous work mainly focuses on the transition relationship between
items during an ongoing session. They generally fail to pay enough attention to
the importance of the items in terms of their relevance to user's main intent.
In this paper, we propose a Session-based Recommendation approach with an
Importance Extraction Module, i.e., SR-IEM, that considers both a user's
long-term and recent behavior in an ongoing session. We employ a modified
self-attention mechanism to estimate item importance in a session, which is
then used to predict user's long-term preference. Item recommendations are
produced by combining the user's long-term preference and current interest as
conveyed by the last interacted item. Experiments conducted on two benchmark
datasets validate that SR-IEM outperforms the start-of-the-art in terms of
Recall and MRR and has a reduced computational complexity
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Streaming session-based recommendation (SSR) is a challenging task that
requires the recommender system to do the session-based recommendation (SR) in
the streaming scenario. In the real-world applications of e-commerce and social
media, a sequence of user-item interactions generated within a certain period
are grouped as a session, and these sessions consecutively arrive in the form
of streams. Most of the recent SR research has focused on the static setting
where the training data is first acquired and then used to train a
session-based recommender model. They need several epochs of training over the
whole dataset, which is infeasible in the streaming setting. Besides, they can
hardly well capture long-term user interests because of the neglect or the
simple usage of the user information. Although some streaming recommendation
strategies have been proposed recently, they are designed for streams of
individual interactions rather than streams of sessions. In this paper, we
propose a Global Attributed Graph (GAG) neural network model with a Wasserstein
reservoir for the SSR problem. On one hand, when a new session arrives, a
session graph with a global attribute is constructed based on the current
session and its associate user. Thus, the GAG can take both the global
attribute and the current session into consideration to learn more
comprehensive representations of the session and the user, yielding a better
performance in the recommendation. On the other hand, for the adaptation to the
streaming session scenario, a Wasserstein reservoir is proposed to help
preserve a representative sketch of the historical data. Extensive experiments
on two real-world datasets have been conducted to verify the superiority of the
GAG model compared with the state-of-the-art methods
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Session-based recommendation (SBR) focuses on next-item prediction at a
certain time point. As user profiles are generally not available in this
scenario, capturing the user intent lying in the item transitions plays a
pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the
item transitions as pairwise relations, which neglect the complex high-order
information among items. Hypergraph provides a natural way to capture
beyond-pairwise relations, while its potential for SBR has remained unexplored.
In this paper, we fill this gap by modeling session-based data as a hypergraph
and then propose a hypergraph convolutional network to improve SBR. Moreover,
to enhance hypergraph modeling, we devise another graph convolutional network
which is based on the line graph of the hypergraph and then integrate
self-supervised learning into the training of the networks by maximizing mutual
information between the session representations learned via the two networks,
serving as an auxiliary task to improve the recommendation task. Since the two
types of networks both are based on hypergraph, which can be seen as two
channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel
Hypergraph Convolutional Networks). Extensive experiments on three benchmark
datasets demonstrate the superiority of our model over the SOTA methods, and
the results validate the effectiveness of hypergraph modeling and
self-supervised task. The implementation of our model is available at
https://github.com/xiaxin1998/DHCNComment: 9 pages, 4 figures, accepted by AAAI'2
Discreetly Exploiting Inter-session Information for Session-based Recommendation
Limited intra-session information is the performance bottleneck of the early
GNN based SBR models. Therefore, some GNN based SBR models have evolved to
introduce additional inter-session information to facilitate the next-item
prediction. However, we found that the introduction of inter-session
information may bring interference to these models. The possible reasons are
twofold. First, inter-session dependencies are not differentiated at the
factor-level. Second, measuring inter-session weight by similarity is not
enough. In this paper, we propose DEISI to solve the problems. For the first
problem, DEISI differentiates the types of inter-session dependencies at the
factor-level with the help of DRL technology. For the second problem, DEISI
introduces stability as a new metric for weighting inter-session dependencies
together with the similarity. Moreover, CL is used to improve the robustness of
the model. Extensive experiments on three datasets show the superior
performance of the DEISI model compared with the state-of-the-art models
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