AM-Bi-LSTM: Adaptive Multi-Modal Bi-LSTM for Sequential Recommendation
Kazuma Ohtomo,
Ryosuke Harakawa,
Masaki Iisaka
et al.
Abstract:Conventional methods for the early fusion of multi-modal features cannot recognize the relevant modality corresponding to the demand of each user in sequential recommendation. In this paper, we propose the adaptive multi-modal bidirectional long short-term memory network (AM-Bi-LSTM) to recognize the relevant modality for sequential recommendation. Specifically, we construct a new recurrent neural network model that is based on the bidirectional long short-term memory network and obtains multimodal features, i… Show more
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