T Liu, Q Wu, L Chang, T Gu - Artificial Intelligence Review, 2022 - Springer
While the recent emergence of a large number of online course resources has made life more convenient for many people, it has also caused information overload. According to a …
Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the …
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item …
X He, K Deng, X Wang, Y Li, Y Zhang… - Proceedings of the 43rd …, 2020 - dl.acm.org
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional …
Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (eg, e-commerce …
Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and …
Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and …
L Wu, J Li, P Sun, R Hong, Y Ge… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in …