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 …
Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, ie, the confounding …
T Wang, L Xia, C Huang - arXiv preprint arXiv:2305.12685, 2023 - arxiv.org
Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to …
H Park, H Jeon, J Kim, B Ahn, U Kang - arXiv preprint arXiv:1710.07134, 2017 - arxiv.org
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 …
With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance …
Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users' conformity towards popular items …
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 …
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 …
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become …