Deep Learning for Search and Recommendation

Liu, W; Xie, K; Pang, L; Bailey, J; Cao, L; Zhang, Y

Deep Learning for Search and Recommendation

Liu, W

Xie, K Pang, L Bailey, J Cao, L Zhang, Y

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2022, pp. 5171-5172
Issue Date:: 2022-10-17

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Field	Value	Language
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313
dc.contributor.author	Xie, K
dc.contributor.author	Pang, L
dc.contributor.author	Bailey, J
dc.contributor.author	Cao, L
dc.contributor.author	Zhang, Y
dc.date.accessioned	2023-03-30T12:46:11Z
dc.date.available	2023-03-30T12:46:11Z
dc.date.issued	2022-10-17
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2022, pp. 5171-5172
dc.identifier.isbn	9781450392365
dc.identifier.uri	http://hdl.handle.net/10453/168859
dc.description.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.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings
dc.relation.ispartof	Proceedings of the 31st ACM International Conference on Information & Knowledge Management
dc.relation.isbasedon	10.1145/3511808.3557493
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Learning for Search and Recommendation
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-30T12:46:10Z
pubs.publication-status	Published

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.

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http://hdl.handle.net/10453/168859