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 …
Z Wang, M Gao, W Li, J Yu, L Guo, H Yin - Proceedings of the 29th ACM …, 2023 - dl.acm.org
The acquisition of explicit user feedback (eg, ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit …
Learning disentangled representations for user intentions from multi-feedback (ie, positive and negative feedback) can enhance the accuracy and explainability of recommendation …
Most of recent neural network (NN)-based recommendation techniques mainly focus on improving the overall performance, such as hit ratio for top-N recommendation, where the …
In recommender systems, click behaviors play a fundamental role in mining users' interests and training models (clicked items as positive samples). Such signals are implicit feedback …
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 …
M Wang, M Gong, X Zheng… - Advances in neural …, 2018 - proceedings.neurips.cc
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are\emph {missing …
Recommendation is prevalently studied for implicit feedback recently, but it seriously suffers from the lack of negative samples, which has a significant impact on the training of …
M Yang, J Wang, JF Ton - Proceedings of the 46th international ACM …, 2023 - dl.acm.org
The issue of fairness in recommendation systems has recently become a matter of growing concern for both the academic and industrial sectors due to the potential for bias in machine …