We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N rec- ommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learn... || 离散化向量；海明相似度（向量计算法）；最优化排序指标; || Fangyuan Luo , Jun Wu∗ , Tao Wang...
Traditional recommendation usually focuses on uti- lizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior rec- ommendation often emphasize the differences be- tween multiple behaviors, i.e., they aim t... || GCN; Contrastive learning; Multi-behavior; || Shuyun Gu , Xiao Wang , Chuan Shi∗ and Ding Xiao...
In this paper, we investigated how to exploit the dynamic mutual influence for enhancing the prediction of social event participation. A unique characteristic of our method is that the social influence is integrated into the threshold calculation for the discriminant function, which reflects the dyn... || 用户组/社交影响；动态阈值学习；长期决定可能由于偏好、短期决定可能仅仅因为社交因素；优化函数设计类；非神经网络设计; || Tong Xu; Hui Xiong...
With the revival of neural networks, many studies try to adapt powerful sequential neural models, i.e., Recurrent Neural Networks
(RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although
the state vector is able to encode se... || 序列推荐系统+ 知识图谱; || Jin Huang...
