Deep Learning for Search Recommendation (DL4SR 2022)
Workshop held in conjunction with CIKM 2022
Workshop held in conjunction with CIKM 2022
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.
Topics of interest on Deep Learning approaches for eCommerce and marketing include, but are not limited to:
- Deep learning based search models
- Deep learning based recommendation models
- Deep learning based recommendation optimization models (e.g. deep reinforcement learning 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).
- Query Understanding: Query intent, Query correction, Query suggestion, Query expansion, multi-modalities queries as well as Query embedding, classification 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.
Submission link: https://easychair.org/conferences/?conf=dl4sr22
Paper Submission Deadline: August 15, 2022, 11:59 PM AoE.
Paper Notification: September 15, 2022, 11:59 PM AoE.
Camera Ready Version: September 30, 2022, 11:59 PM AoE.
Full-Day Workshop: October 21, 2022
This workshop follows the submission requirements of the CIKM conference.
- 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.
- Single-blind peer review
- 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.
- Yingyi Zhang, Xianneng Li, Yahe Yu, Jian Tang, Huanfang Deng, Junya Lu, Yeyin Zhang, Qiancheng Jiang, Yunsen Xian and Liqian Yu. Entire Cost Enhanced Multi-Task Model for Online-to-Offline Conversion Rate Prediction(PDF)
- Gautam Kumar and Chikara Hashimoto. A Two-Phased Approach to Training Data Generation for Shopping Query Intent Prediction (PDF)
- Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta and Claudio Pomo. Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews (PDF)
- Debanjan Mahata, Navneet Agarwal, Dibya Gautam, Amardeep Kumar, Swapnil Parekh, Yaman Kumar Singla, Anish Acharya and Rajiv Ratn Shah. LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents (PDF)
- Osayande P. Omondiagbe, Sherlock A Licorish and Stephen G. MacDonell. Preventing Negative Transfer on Sentiment Analysis in Deep Transfer Learning (PDF)
- Tesi Xiao, Xia Xiao, Ming Chen and Youlong Chen. Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction (PDF)
- Yanchao Tan, Carl Yang, Xiangyu Wei, Ziyue Wu, Weiming Liu and Xiaolin Zheng. Partial Relaxed Optimal Transport for Denoised Recommendation (PDF)
- Shaochuan Lin, Yicong Yu, Xiyu Ji, Taotao Zhou, Hengxu He, Zisen Sang, Jia Jia, Guodong Cao and Ning Hu. Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services (PDF)
- Tajuddeen Gwadabe, Mohammed Ali Al-Hababi and Ying Liu. Session-based Recommendation with Dual Graph Networks (PDF)
- Qian Yu, Xiangdong Wu, Chen Yang, Zihao Zhao, Haoxin Liu, Chaosheng Fan, Changping Peng, Zhangang Lin, Jinghe Hu and Jingping Shao. Exploiting Global Behavior Contextual Correlation in Sequential Recommendation Augmentation (PDF)
- Xue Li, Wei Shen and Denis Charles. TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification (PDF)
- Xianjing Liu, Behzad Golshan, Kenny Leung, Aman Saini, Vivek Kulkarni and Ali Mollahosseini. TWICE - Twitter Content Embeddings (PDF)
|08:35AM - 08:40AM, 2022/10/21 (EDT)||Host Chair||Welcome and Open Remarks|
|08:40AM - 09:25AM, 2022/10/21 (EDT)||Xiuzhen (Jenny) Zhang [Professor of RMIT University, Australia]||Keynote 1: Responsible Recommendation of Information Items: Veracity, Fairness, and Trust|
|09:25AM - 09:35AM, 2022/10/21 (EDT)||Qian Yu, Xiangdong Wu and Chen Yang et.al||Paper-1 :Exploiting Global Behavior Contextual Correlation in Sequential Recommendation Augmentation|
|09:35AM - 09:45AM, 2022/10/21 (EDT)||Tajuddeen Gwadabe, Mohammed Ali Al-Hababi and Ying Liu||Paper-2 : Session-based Recommendation with Dual Graph Networks|
|09:45AM - 10:20AM, 2022/10/21 (EDT)||Jonathan Purnell[VP of Data Science at Spectrum Labs]||Keynote 2: A Deep Learning Virtuous Cycle for User Understanding|
|09:45AM - 10:00AM, 2022/10/21 (EDT)||Yingyi Zhang, Xianneng Li and Yahe Yu et.al||Paper-3 : Entire Cost Enhanced Multi-Task Model for Online-to-Offline Conversion Rate Prediction|
|10:00AM - 10:15AM, 2022/10/21 (EDT)||Coffee Break|
|10:15AM -10:30AM, 2022/10/21 (EDT)||Gautam Kumar and Chikara Hashimoto||Paper-4 : A Two-Phased Approach to Training Data Generation for Shopping Query Intent Prediction|
|10:30AM - 10:45AM, 2022/10/21 (EDT)||Yanchao Tan, Carl Yang and Xiangyu Wei et.al||Paper-5 :Partial Relaxed Optimal Transport for Denoised Recommendation|
|10:45AM - 11:00AM, 2022/10/21 (EDT)||Shaochuan Lin, Yicong Yu and Xiyu Ji et.al||Paper-6 :Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services|
|11:00AM - 11:30AM, 2022/10/21 (EDT)||Belinda Zeng[GM, Head of Applied Science and Engineering - Amazon Search Science and AI]||Keynote 3: Scaling, Strengthening and Serving: Innovating through Large-Scale Deep Representations in E-commerce Product Search and Recommendation Applications|
|11:30AM - 12:15AM, 2022/10/21 (EDT)||Ed H. Chi [Distinguished Scientist at Google]||Keynote 4: Beyond Being Accurate: Neural Modeling and Reinforcement Learning for Large-Scale Real-World Recommendation Problems|
|12:15AM - 13:15PM, 2022/10/21 (EDT)||Lunch Break|
|13:15PM - 13:50PM, 2022/10/21 (EDT)||Caiming Xiong[VP of AI Research at Salesforce]||Keynote 5: A Path Towards More Universal Question Answering Systems||13:50PM - 14:05PM, 2022/10/21 (EDT)||Vito Walter Anelli, Yashar Deldjoo and Tommaso Di Noia et.al||Paper-7 : Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews|
|14:05PM - 14:15PM, 2022/10/21 (EDT)||Debanjan Mahata, Navneet Agarwal and Dibya Gautam et.al||Paper-8 : LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents|
|14:15PM - 14:30PM, 2022/10/21 (EDT)||Osayande P. Omondiagbe, Sherlock A Licorish and Stephen G. MacDonell.||Paper-9 : Preventing Negative Transfer on Sentiment Analysis in Deep Transfer Learning|
|14:30PM - 14:45PM, 2022/10/21 (EDT)||Tesi Xiao, Xia Xiao, Ming Chen and Youlong Chen.||Paper-10 : Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction|
|14:45PM - 14:55PM, 2022/10/21 (EDT)||Xianjing Liu, Behzad Golshan, Kenny Leung, Aman Saini, Vivek Kulkarni, Ali Mollahosseini and Jeff Motd>||Paper-11 : TWICE - Twitter Content Embeddings|
|14:55PM - 15:05PM, 2022/10/21 (EDT)||Xue Li, Wei Shen and Denis Charles||Paper-12 : TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification|
|15:05PM - 15:10PM, 2022/10/21 (EDT)||Closing Remarks|
Philippe Laban, Salesforce
Zhensong Qian, Amazon
Donglin Hu , Salesforce
Xinghao Yang, University of Technology Sydney
Ali Braytee, University of Technology, Sydney
Aneesh Chivukula, BITS Pilani
Mingze Ni, University of Technology, Sydney
Vito Ostuni, Netflix
Xianjing Liu, Twitter Inc.
Xue Li, Microsoft
Raghavendra Chalapathy, Walmart Labs