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abstract

Deep Learning for Search and Recommendation

Published: 17 October 2022 Publication History

Abstract

In the current digital world, web search engines and recommendation systems are continuously evolving, opening up new potential challenges every day which require more sophisticated and efficient data mining and machine learning solutions to satisfy the needs of sellers and consumers as well as marketers. The quality of search and recommendation systems impacts customer retention, time on site, and sales volume. For instance, with often sparse conversion rates, highly personalized contents, heterogeneous digital sources, more rigorous and effective models are required to be developed by research engineers and data scientists. At the same time, deep learning has started to show great impact in many industrial applications which are capable of processing complicated, large-scale and real-time data. Deep learning not only provides more opportunities to increase conversion rates and improve revenue through a positive customer experience, but also provides customers with personalized contents along with their personal shopping journey. Due to this rapid growth of the digital world, there is a need to bring professionals together from both academic research and the industry to solve real-world problems. This workshop fosters the development of a strong research community focused on solving deep learning based large-scale web search, personalized search, recommendation and ranking relevance problems that provide superior digital experience to all users.

References

[1]
Quan Do, Wei Liu, Jin Fan, and Dacheng Tao. 2019. Unveiling hidden implicit similarities for cross-domain recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 1 (2019), 302--315.
[2]
Goce Ristanoski, Wei Liu, and James Bailey. 2013. A time-dependent enhanced support vector machine for time series regression. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[3]
Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence.

Cited By

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  • (2024)Student Support System with Real-Time Face Recognition Based Attendance System and Course Recommendation Engine with Analytical Study Strategy Visualization2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531496(1-8)Online publication date: 15-Mar-2024

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Published In

cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2022

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Author Tags

  1. deep learning
  2. recommender systems
  3. web search

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CIKM '22
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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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View all
  • (2024)Student Support System with Real-Time Face Recognition Based Attendance System and Course Recommendation Engine with Analytical Study Strategy Visualization2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531496(1-8)Online publication date: 15-Mar-2024

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