Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GraphSW: a training protocol based on stage-wise training for GNN-based
  Recommender Model   Recently, researchers utilize Knowledge Graph (KG) as side information in
recommendation system to address cold start and sparsity issue and improve the
recommendation performance. Existing KG-aware recommendation model use the
feature of neighboring entities and structural information to update the
embedding of currently located entity. Although the fruitful information is
beneficial to the following task, the cost of exploring the entire graph is
massive and impractical. In order to reduce the computational cost and maintain
the pattern of extracting features, KG-aware recommendation model usually
utilize fixed-size and random set of neighbors rather than complete information
in KG. Nonetheless, there are two critical issues in these approaches: First of
all, fixed-size and randomly selected neighbors restrict the view of graph. In
addition, as the order of graph feature increases, the growth of parameter
dimensionality of the model may lead the training process hard to converge. To
solve the aforementioned limitations, we propose GraphSW, a strategy based on
stage-wise training framework which would only access to a subset of the
entities in KG in every stage. During the following stages, the learned
embedding from previous stages is provided to the network in the next stage and
the model can learn the information gradually from the KG. We apply stage-wise
training on two SOTA recommendation models, RippleNet and Knowledge Graph
Convolutional Networks (KGCN). Moreover, we evaluate the performance on six
real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015,
Amazon-book and Yelp 2018. The result of our experiments shows that proposed
strategy can help both models to collect more information from the KG and
improve the performance. Furthermore, it is observed that GraphSW can assist
KGCN to converge effectively in high-order graph feature.

13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th