DL4SR22: Deep Learning for Search and Recommendation |
Website | https://dl4sr.github.io/dl4sr22/ |
Submission link | https://easychair.org/conferences/?conf=dl4sr22 |
Abstract registration deadline | August 15, 2022 |
Submission deadline | August 15, 2022 |
Deep Learning for Search Recommendation (DL4SR 2022) : https://dl4sr.github.io/dl4sr22/
To be held in conjunction with CIKM 2022 (https://www.cikm2022.org/)
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 is exactly what this workshop aims to achieve. Topics of this workshop include deep learning based query understanding, personalization, representation learning, product retrieval, recommendation algorithm, ranking algorithms, etc.
We invite quality research contributions, industrial achievement addressing relevant deep learning challenges in the domain of search, recommendation and personalization. We invite submission of long and short papers of two to eight pages (including references), representing original research, preliminary research results, proposals for new work in academic or industry. All submissions will be single-blind and will be peer reviewed by an international program committee of researchers/industrial professionals with high reputation. Accepted submissions will be required to be presented at the workshop.
Submission Guidelines
Paper Submission Deadline: August 15, 2022, 11:59 PM AoE.
This workshop follows the submission requirements of the CIKM conference.
Rules:
- Long papers are up to 8 pages and short papers are up to 4 pages. These page limits include the bibliography and any possible appendices.
- All papers must be formatted according to the ACM sigconf template manuscript style, following the submission guidelines available at: https://www.acm.org/publications/proceedings-template.
- Papers should be submitted in PDF format, electronically, using the EasyChair submission system.
Publication: Accepted papers in this workshop may be included in the DL4SR 2022 Workshop Proceedings published online by CEUR.
List of Topics
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Topics of interest on Deep Learning approaches for eCommerce and marketing include, but are not limited to:
- Query Understanding: Query intent, Query correction, Query suggestion, Query expansion, multi-modalities queries as well as Query embedding, classification etc
- Recommendation and Personalization: user historical behavior-based, content-based etc.
- Representations Learning: various deep representations of products, queries, and customers including knowledge graph, embedding etc.
- Retrieval models and ranking (e.g., ranking algorithms, learning to rank, NLP models, retrieval models, etc).
- Deep learning based search models
- Deep learning based recommendation and generation models
- Deep learning based recommendation optimization models (e.g. deep reinforcement learning etc.)
- Privacy issues in search and/or recommendation models
- Multimodal search and/or recommendation models
- Heterogeneous data analysis in search and/or recommendation models
- Industrial domain-specific applications of search, recommendation models
- Improved model for customer engagement in marketing etc.
Committees
Organizing committee
- Wei Liu: University of Technology Sydney
- Kexin Xie: Salesforce
- Linsey Pang: Salesforce
- James Bailey: The University of Melbourne
- Longbing Cao: University of Technology Sydney
- Yuxi Zhang: Salesforce
Contact
All questions about submissions should be emailed to dl4sr22@googlegroups.com