Be causal: De-biasing social network confounding in recommendation
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem
results in the selection bias issue, degrading the recommendation performance ultimately. A …
results in the selection bias issue, degrading the recommendation performance ultimately. A …
A modular adversarial approach to social recommendation
A Krishnan, H Cheruvu, C Tao… - Proceedings of the 28th …, 2019 - dl.acm.org
This paper proposes a novel framework to incorporate social regularization for item
recommendation. Social regularization grounded in ideas of homophily and influence …
recommendation. Social regularization grounded in ideas of homophily and influence …
Deconfounded recommendation via causal intervention
Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …
correlations between user/item profiles and user preference prediction, ie, the confounding …
Denoised self-augmented learning for social recommendation
Social recommendation is gaining increasing attention in various online applications,
including e-commerce and online streaming, where social information is leveraged to …
including e-commerce and online streaming, where social information is leveraged to …
Uniwalk: Explainable and accurate recommendation for rating and network data
How can we leverage social network data and observed ratings to correctly recommend
proper items and provide a persuasive explanation for the recommendations? Many online …
proper items and provide a persuasive explanation for the recommendations? Many online …
Social boosted recommendation with folded bipartite network embedding
With the prevalence of online social platforms, social recommendation has emerged as a
promising direction that leverages the social network among users to enhance …
promising direction that leverages the social network among users to enhance …
Disentangling user interest and conformity for recommendation with causal embedding
Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …
observational interaction data could result from users' conformity towards popular items …
Debiasing recommendation by learning identifiable latent confounders
Recommendation systems aim to predict users' feedback on items not exposed to them yet.
Confounding bias arises due to the presence of unmeasured variables (eg, the socio …
Confounding bias arises due to the presence of unmeasured variables (eg, the socio …
Modelling high-order social relations for item recommendation
The prevalence of online social network makes it compulsory to study how social relations
affect user choice. However, most existing methods leverage only first-order social relations …
affect user choice. However, most existing methods leverage only first-order social relations …
Socialgcn: An efficient graph convolutional network based model for social recommendation
Collaborative Filtering (CF) is one of the most successful approaches for recommender
systems. With the emergence of online social networks, social recommendation has become …
systems. With the emergence of online social networks, social recommendation has become …