| CPC G06F 18/24137 (2023.01) [G06F 18/23213 (2023.01); G06N 20/00 (2019.01)] | 15 Claims |
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1. A method for sequential recommendation based on user intent modeling, the method comprising:
receiving a plurality of user behavior sequences;
encoding, via an encoder, the plurality of user behavior sequences into a plurality of user interest representations;
clustering the plurality of user interest representations into a plurality of clusters based on mutual distances among the user interest representations in a representation space;
determining a plurality of intention prototypes based on centroids of the plurality of clusters;
constructing a set of augmented views for a first user behavior sequence from the plurality of user behavior sequences;
encoding, via the encoder, the set of augmented views into a set of view representations;
computing a contrastive loss based on a summation of user contrastive losses corresponding to a number of users, wherein each user contrastive loss is computed based on a first similarity between a first positive view representation and an intention prototype of the plurality of intention prototypes corresponding to a respective user, and a plurality of similarities between the first positive view representation and a set of intention prototypes of the plurality of intention prototypes that do not correspond to the respective user; and
iteratively updating the encoder to minimize the contrastive loss alone or in weighted combination with an additional loss component.
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