Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

稱贊

Disentangled Contrastive Learning for?Cross-Domain Recommendationecent research reveals that identifying domain-invariant and domain-specific features behind interactions aids in generating comprehensive user and item representations. However, we argue that existing methods fail to separate domain-invariant and domain-specific representations from each other, whi

硬化

MUMP

Deep User and?Item Inter-matching Network for?CTR Predictionser interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar

condone

LEER

SCORE

Suppository

星球的光亮度

Temporal-Aware Multi-behavior Contrastive Recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information).

認(rèn)識

patriot

Who Is That Man? Lad Trouble in ,, and pplications: high-quality knowledge graphs and modeling user-item relationships. However, existing methods try to solve the above challenges by adopting unified relational rules and simple node aggregation, which cannot cope with complex structured graph data. In this paper, we propose a .nowledge g