Neurjudge: A circumstance-aware neural framework for legal judgment prediction
Legal Judgment Prediction is a fundamental task in legal intelligence of the civil law system,
which aims to automatically predict the judgment results of multiple subtasks, such as …
which aims to automatically predict the judgment results of multiple subtasks, such as …
Deep interest highlight network for click-through rate prediction in trigger-induced recommendation
In many classical e-commerce platforms, personalized recommendation has been proven to
be of great business value, which can improve user satisfaction and increase the revenue of …
be of great business value, which can improve user satisfaction and increase the revenue of …
Learning fine-grained user interests for micro-video recommendation
Recent years have witnessed the rapid development of online micro-video platforms, in
which the recommender system plays an essential role in overcoming the information …
which the recommender system plays an essential role in overcoming the information …
Stock trend prediction with multi-granularity data: A contrastive learning approach with adaptive fusion
Stock trend prediction plays a crucial role in quantitative investing. Given the prediction task
on a certain granularity (eg, daily trend), a large portion of existing studies merely leverage …
on a certain granularity (eg, daily trend), a large portion of existing studies merely leverage …
RI-GCN: Review-aware interactive graph convolutional network for review-based item recommendation
Y Cai, Y Wang, W Wang, W Chen - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
A wealth of semantic features exist in the reviews written by users, such as rich information
on item features and implicit preferences of users. Existing review-based recommendation …
on item features and implicit preferences of users. Existing review-based recommendation …
Towards automatic discovering of deep hybrid network architecture for sequential recommendation
Recent years have witnessed great success in deep learning-based sequential
recommendation (SR), which can provide more timely and accurate recommendations. One …
recommendation (SR), which can provide more timely and accurate recommendations. One …
Clustering based behavior sampling with long sequential data for CTR prediction
Click-through rate (CTR) prediction is fundamental in many industrial applications, such as
online advertising and recommender systems. With the development of the online platforms …
online advertising and recommender systems. With the development of the online platforms …
A click-through rate model of e-commerce based on user interest and temporal behavior
Y **ao, WK He, Y Zhu, J Zhu - Expert Systems with Applications, 2022 - Elsevier
In the advertising and marketing of e-commerce platform, click rate prediction is directly
related to the revenue of e-commerce platform. In this paper, we propose an advertising click …
related to the revenue of e-commerce platform. In this paper, we propose an advertising click …
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance,
in which feature interaction selection plays a key role. Most approaches model interactions …
in which feature interaction selection plays a key role. Most approaches model interactions …
Triangle graph interest network for click-through rate prediction
W Jiang, Y Jiao, Q Wang, C Liang, L Guo… - Proceedings of the …, 2022 - dl.acm.org
Click-through rate prediction is a critical task in online advertising. Currently, many existing
methods attempt to extract user potential interests from historical click behavior sequences …
methods attempt to extract user potential interests from historical click behavior sequences …