Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Incremental Graph Convolution for Recommender System Retraining   Real-world recommender system needs to be regularly retrained to keep with
the new data. In this work, we consider how to efficiently retrain graph
convolution network (GCN) based recommender models, which are state-of-the-art
techniques for collaborative recommendation. To pursue high efficiency, we set
the target as using only new data for model updating, meanwhile not sacrificing
the recommendation accuracy compared with full model retraining. This is
non-trivial to achieve, since the interaction data participates in both the
graph structure for model construction and the loss function for model
learning, whereas the old graph structure is not allowed to use in model
updating. Towards the goal, we propose a \textit{Causal Incremental Graph
Convolution} approach, which consists of two new operators named
\textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect
Distillation} (CED) to estimate the output of full graph convolution. In
particular, we devise simple and effective modules for IGC to ingeniously
combine the old representations and the incremental graph and effectively fuse
the long-term and short-term preference signals. CED aims to avoid the
out-of-date issue of inactive nodes that are not in the incremental graph,
which connects the new data with inactive nodes through causal inference. In
particular, CED estimates the causal effect of new data on the representation
of inactive nodes through the control of their collider. Extensive experiments
on three real-world datasets demonstrate both accuracy gains and significant
speed-ups over the existing retraining mechanism.

10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an