Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Disentanglement with Network Information for Debiased
  Recommendations   Recommender systems aim to recommend new items to users by learning user and
item representations. In practice, these representations are highly entangled
as they consist of information about multiple factors, including user's
interests, item attributes along with confounding factors such as user
conformity, and item popularity. Considering these entangled representations
for inferring user preference may lead to biased recommendations (e.g., when
the recommender model recommends popular items even if they do not align with
the user's interests).
  Recent research proposes to debias by modeling a recommender system from a
causal perspective. The exposure and the ratings are analogous to the treatment
and the outcome in the causal inference framework, respectively. The critical
challenge in this setting is accounting for the hidden confounders. These
confounders are unobserved, making it hard to measure them. On the other hand,
since these confounders affect both the exposure and the ratings, it is
essential to account for them in generating debiased recommendations. To better
approximate hidden confounders, we propose to leverage network information
(i.e., user-social and user-item networks), which are shown to influence how
users discover and interact with an item. Aside from the user conformity,
aspects of confounding such as item popularity present in the network
information is also captured in our method with the aid of \textit{causal
disentanglement} which unravels the learned representations into independent
factors that are responsible for (a) modeling the exposure of an item to the
user, (b) predicting the ratings, and (c) controlling the hidden confounders.
Experiments on real-world datasets validate the effectiveness of the proposed
model for debiasing recommender systems.

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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin