Titlebook: Web Information Systems Engineering – WISE 2021; 22nd International C Wenjie Zhang,Lei Zou,Lu Chen Conference proceedings 2021 Springer Nat

impale

BOOR

Capturing Multi-granularity Interests with Capsule Attentive Network for Sequential Recommendation intricate sequential dependencies and user’s various interests underneath the interactions. Existing works regard each item that the user interacts with as an interest unit and apply advanced deep learning techniques to learn a unified interest representation. However, user’s interests vary in mult

頭盔

Multi-Task Learning with Personalized Transformer for Review Recommendationor popularity order without personalization. The review recommendation model provides users with attractive reviews and efficient consumption experience, allowing users to grasp the characteristics of items in seconds. However, the sparsity of interactions between users and reviews appears to be a m

Eosinophils

Multi-Task Learning with Personalized Transformer for Review Recommendationor popularity order without personalization. The review recommendation model provides users with attractive reviews and efficient consumption experience, allowing users to grasp the characteristics of items in seconds. However, the sparsity of interactions between users and reviews appears to be a m

Acumen

大廳

foppish

脆弱么

MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendationods usually exploit the individual-level POI sequences but failed to utilize the information of collective-level POI sequences. Since collective-level POIs, like shopping malls or plazas, are common in the real world, we argue that only the individual-level POI sequences cannot represent more semant

TEM

HRFA: Don’t Ignore Strangers with Different Views methods resort to supplementary reviews written by similar users, which only leverage homogeneous preferences. However, users holding different views could also supply valuable information with heterogeneous preferences. In this paper, we propose a recommendation model for rating prediction, named

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