Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-level Cross-view Contrastive Learning for Knowledge-aware
  Recommender System   Knowledge graph (KG) plays an increasingly important role in recommender
systems. Recently, graph neural networks (GNNs) based model has gradually
become the theme of knowledge-aware recommendation (KGR). However, there is a
natural deficiency for GNN-based KGR models, that is, the sparse supervised
signal problem, which may make their actual performance drop to some extent.
Inspired by the recent success of contrastive learning in mining supervised
signals from data itself, in this paper, we focus on exploring the contrastive
learning in KG-aware recommendation and propose a novel multi-level cross-view
contrastive learning mechanism, named MCCLK. Different from traditional
contrastive learning methods which generate two graph views by uniform data
augmentation schemes such as corruption or dropping, we comprehensively
consider three different graph views for KG-aware recommendation, including
global-level structural view, local-level collaborative and semantic views.
Specifically, we consider the user-item graph as a collaborative view, the
item-entity graph as a semantic view, and the user-item-entity graph as a
structural view. MCCLK hence performs contrastive learning across three views
on both local and global levels, mining comprehensive graph feature and
structure information in a self-supervised manner. Besides, in semantic view, a
k-Nearest-Neighbor (kNN) item-item semantic graph construction module is
proposed, to capture the important item-item semantic relation which is usually
ignored by previous work. Extensive experiments conducted on three benchmark
datasets show the superior performance of our proposed method over the
state-of-the-arts. The implementations are available at:
https://github.com/CCIIPLab/MCCLK.

15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph