Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

19 Sep 2018Jiaxi TangKe Wang

Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item... (read more)

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