In the self-attentive sequential recommendation,in addition to the huge memory consumption caused by the item embedding matrix and the noise caused by irrelevant information in the self-attention layer,there is also a key problem of how to accurately extract and represent user preferences in the case of sparse user behavior data.A lightweight and denoising self-atten-tive sequential recommendation using preference editing(LDSR-PE)was proposed to solve the above problems.A context-aware dynamic embedding composition scheme was used to alleviate the memory consumption problem,and trainable binary masks were attached to each self-attention layer to achieve adaptive pruning of irrelevant noise items.To better train the model,a self-super-vised learning strategy based on preference editing was designed to force the sequential recommendation model to discriminate common and unique preferences in different sequences of interaction.Results of a large number of experiments conducted on three public datasets show that the LDSR-PE outperforms the mainstream advanced recommendation models.
维普
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