################################################################################# This is the code for paper: A Variational Multi-modal Information Bottleneck Model for Next-item Recommendation ################################################################################# Requirement: python 3.6 tensorflow-gpu==1.14.0 ################################################################################# download the data sets in https://pan.baidu.com/s/1RlVtrP4ytD67MSgBUI0cmQ?pwd=1111 with code: 1111 The main.py describes the hyper-parameter of the proposed method, which can be summaried as follows: d: The dimension of embeddings. F: The number of most frequent patterns. K: Users’ memory capacity. lamda: the trade-off coefficient in Eq.(2). sVAE: M3IB-noM2PG cVAE: M3IB-noMM 'KL': M3IB 'no-KL': M3IB-noBN
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