XMRec: Workshop on Cross-Market Recommendation
Co-located with RecSys 2021, Virtual Event, October 2, 2021
Homepage: http://xmrec.github.io <http://xmrec.github.io/>
Registration Form: https://forms.gle/jqThinxGFerQF5cEA <https://forms.gle/jqThinxGFerQF5cEA>
For queries: m.aliannejadi at uva.nl <mailto:m.aliannejadi at uva.nl> ===
Online markets are spreading quickly across the globe, supporting a huge network of product sales to billions of customers with various cultures, lifestyles, economic interests, and languages. These global markets introduce many novel opportunities---as well as challenges. The Workshop on Cross-Market Recommendation (XMRec) concerns various tasks and problems related to recommending relevant products to users in a target market (e.g., a resource-scarce market) by leveraging data from similar high-resource markets, e.g. using data from the U.S. market to improve recommendations in a target market. We hypothesize that data from one market can be used to improve recommendations in another.
We aim to create a dynamic and interactive atmosphere where researchers of diverse backgrounds and interests can discuss their ideas on cross-market recommendations and how they can be further pursued in the community. To this end, XMRec features a series of seed talks both from industry and academia, discussing the future of cross-market recommendation and its potentials as a new line of research. The seed talks will be followed by a panel discussion where a diverse set of researchers discuss their ideas and opinion about the topic. Finally, we will invite the participants and the panelists to take part in interactive brainstorming breakout sessions to further discuss their ideas. We aim to motivate a range of studies (like analyzing market-specific biases, conversational recommendation, and predicting early adopters) beyond the cross-domain recommendation by extending markets and content languages.
For further information and updates, please visit the workshop's website: http://xmrec.github.io <http://xmrec.github.io/>
• Julian McAuley, UCSD, USA
• Ben Carterette, Spotify, USA
• Rahul Bhagat, Amazon Inc., USA
• Ben Carterette, Spotify, USA
• Elisabeth Lex, Graz University of Technology, Austria
• Max Harper, Amazon Inc., USA
CEST EST PST China Time Program 15:00 - 15:10 09:00 - 09:10 06:00 - 06:10 21:00 - 21:10 Openning 15:10 - 15:55 09:10 - 09:55 06:10 - 06:55 21:10 - 21:55 Keynote by Ben Carterette 15:55 - 16:05 09:55 - 10:05 06:55 - 07:05 21:55 - 22:05 Short break 16:05 - 16:50 10:05 - 10:50 07:05 - 07:50 22:05 - 22:50 Keynote by Julian McAuley 16:50 - 17:15 10:50 - 11:15 07:50 - 08:15 22:50 - 23:15 Long break and poster presentation of the accepted paper 17:15 - 17:35 11:15 - 11:35 08:15 - 08:35 23:15 - 23:35 Introduction to XMRec topics and panel discussion 17:35 - 18:50 11:35 - 12:50 08:35 - 09:50 23:35 - 00:50 Panel discussion 18:50 - 19:00 12:50 - 13:00 09:50 - 10:00 00:50 - 01:00 Closing
All participants of XMRec should be registered at RecSys’21. However, to facilitate communication with the attendees, we kindly ask you to register using the following Google Form if you intend to attend the workshop: https://forms.gle/jqThinxGFerQF5cEA <https://forms.gle/jqThinxGFerQF5cEA>
List of Relevant Topics
Relevant topics include, but are not limited to the following.
• Machine Learning Approaches: research focusing on the application of various machine learning approaches for knowledge transfer such as domain adaptation, semi-supervised learning, transfer learning, data augmentation, meta-learning, and knowledge distillation for the cross-market recommendation.
• Cross-Domain & Cross-Market Recommendation: research focusing on both cross-domain and cross-market techniques.
• Data Selection & Augmentation: research on data selection or augmentation techniques (e.g., from the resource-rich markets) for cross-market training of models.
• Market Similarity Measurement: research on measuring and selecting similar markets to apply market adaptation and/or cross-market training.
• Market-Specific Bias: research on potential biases existing in various markets that could affect the cross-market recommendation.
• Cross-Lingual Content-Based Recommendation: research focusing on leveraging cross-lingual content for recommendation such as item descriptions and user reviews.
• Cold-Start Recommendation: research on leveraging interactions from warm-start markets to address the cold-start problem in other markets.
• Resource: works describing resources that can foster research on the cross-market recommendation.
• Mohammad Aliannejadi, University of Amsterdam, The Netherlands
• Hamed Bonab, University of Massachusetts Amherst, USA
• Ali Vardasbi, University of Amsterdam, The Netherlands
• Evangelos Kanoulas, University of Amsterdam, The Netherlands
• James Allan, University of Massachusetts Amherst, USA
• Vanessa Murdock, Amazon, USA -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 20271 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20210907/749d75aa/attachment.txt>