impale
发表于 2025-3-25 04:59:29
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BOOR
发表于 2025-3-25 09:21:40
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
头盔
发表于 2025-3-25 15:25:57
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
发表于 2025-3-25 19:46:30
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
发表于 2025-3-25 21:13:14
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大厅
发表于 2025-3-26 01:26:39
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foppish
发表于 2025-3-26 05:56:08
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脆弱么
发表于 2025-3-26 11:27:01
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
发表于 2025-3-26 14:03:13
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
发炎
发表于 2025-3-26 19:35:01
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