Recommender systems should answer the intervention question" if recommending an item to a user, what would the feedback be", calling for estimating the causal effect of a …
How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this paper, we first describe the generation process of the biased and …
W Ren, L Wang, K Liu, R Guo… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed …
V Tsintzou, E Pitoura, P Tsaparas - arXiv preprint arXiv:1811.01461, 2018 - arxiv.org
Recommender systems have been applied successfully in a number of different domains, such as, entertainment, commerce, and employment. Their success lies in their ability to …
C Zhou, J Ma, J Zhang, J Zhou, H Yang - Proceedings of the 27th ACM …, 2021 - dl.acm.org
Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial …
While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit …
H Li, Y Xiao, C Zheng, P Wu - Proceedings of the ACM Web Conference …, 2023 - dl.acm.org
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale …
Current recommender systems have achieved great successes in online services, such as E- commerce and social media. However, they still suffer from the performance degradation in …
The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data …