The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training …
Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users' conformity towards popular items …
In this article, we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is …
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over …
Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have …
J Chen, X Wang, F Feng, X He - … of the 15th ACM Conference on …, 2021 - dl.acm.org
Recommender systems (RS) have demonstrated great success in information seeking. Recent years have witnessed a large number of work on inventing recommendation models …
Z Wang, S Shen, Z Wang, B Chen, X Chen… - Proceedings of the ACM …, 2022 - dl.acm.org
Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different …
H Li, Y Xiao, C Zheng, P Wu… - … Conference on Machine …, 2023 - proceedings.mlr.press
Propensity-based weighting methods have been widely studied and demonstrated competitive performance in debiased recommendations. Nevertheless, there are still many …
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction …