12,795 research outputs found
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Heterogeneous information network has been widely used to alleviate sparsity
and cold start problems in recommender systems since it can model rich context
information in user-item interactions. Graph neural network is able to encode
this rich context information through propagation on the graph. However,
existing heterogeneous graph neural networks neglect entanglement of the latent
factors stemming from different aspects. Moreover, meta paths in existing
approaches are simplified as connecting paths or side information between node
pairs, overlooking the rich semantic information in the paths. In this paper,
we propose a novel disentangled heterogeneous graph attention network DisenHAN
for top- recommendation, which learns disentangled user/item representations
from different aspects in a heterogeneous information network. In particular,
we use meta relations to decompose high-order connectivity between node pairs
and propose a disentangled embedding propagation layer which can iteratively
identify the major aspect of meta relations. Our model aggregates corresponding
aspect features from each meta relation for the target user/item. With
different layers of embedding propagation, DisenHAN is able to explicitly
capture the collaborative filtering effect semantically. Extensive experiments
on three real-world datasets show that DisenHAN consistently outperforms
state-of-the-art approaches. We further demonstrate the effectiveness and
interpretability of the learned disentangled representations via insightful
case studies and visualization.Comment: Accepted at CIKM202
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
Structure fusion based on graph convolutional networks for semi-supervised classification
Suffering from the multi-view data diversity and complexity for
semi-supervised classification, most of existing graph convolutional networks
focus on the networks architecture construction or the salient graph structure
preservation, and ignore the the complete graph structure for semi-supervised
classification contribution. To mine the more complete distribution structure
from multi-view data with the consideration of the specificity and the
commonality, we propose structure fusion based on graph convolutional networks
(SF-GCN) for improving the performance of semi-supervised classification.
SF-GCN can not only retain the special characteristic of each view data by
spectral embedding, but also capture the common style of multi-view data by
distance metric between multi-graph structures. Suppose the linear relationship
between multi-graph structures, we can construct the optimization function of
structure fusion model by balancing the specificity loss and the commonality
loss. By solving this function, we can simultaneously obtain the fusion
spectral embedding from the multi-view data and the fusion structure as
adjacent matrix to input graph convolutional networks for semi-supervised
classification. Experiments demonstrate that the performance of SF-GCN
outperforms that of the state of the arts on three challenging datasets, which
are Cora,Citeseer and Pubmed in citation networks
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