Attentive capsule network for click-through rate and conversion rate prediction in online advertising-Reference-Cited by-同舟云学术

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Attentive capsule network for click-through rate and conversion rate prediction in online advertising

Published:2021-01 Issue: Volume:211 Page:106522
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Li Dongfang,Hu Baotian,Chen Qingcai,Wang Xiao,Qi Quanchang,Wang Liubin,Liu Haishan

Funder

Natural Science Foundation of China

Strategic Emerging Industry Development Special Funds of Shenzhen

Tencent Rhino-Bird Elite Training Program

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference47 articles.

1. Wide & deep learning for recommender systems;Cheng,2016

2. Deep interest network for click-through rate prediction;Zhou,2018

3. Optimal real-time bidding for display advertising;Zhang,2014

4. Entire space multi-task model: An effective approach for estimating post-click conversion rate;Ma,2018

5. Multi-level deep cascade trees for conversion rate prediction in recommendation system;Wen,2019

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1. The impacts of coupon and discount on exposure and various stages of conversion funnel: An empirical investigation of the online food delivery service;Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration;2024-09-02

2. Accurate multi-interest modeling for sequential recommendation with attention and distillation capsule network;Expert Systems with Applications;2024-06

3. Deep click interest network for reranking hotels;Engineering Applications of Artificial Intelligence;2024-04

4. A Click Conversion Rate Model of E-Commerce Platforms Aiming at Effective Data Sparse;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-04

5. PeNet: A feature excitation learning approach to advertisement click-through rate prediction;Neural Networks;2024-04

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