- Author
- Year
- 2017
- Title
- SIGIR 2017 Workshop on eCommerce (ECOM17)
- Event
- 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
- Book/source title
- SIGIR'17 : proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Book/source subtitle
- August 7-11, 2017, Shinjuku, Tokyo, Japan
- Pages (from-to)
- 1425-1426
- Number of pages
- 2
- Publisher
- New York, NY: Association for Computing Machinery
- ISBN (electronic)
- 9781450350228
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- Abstract
-
eCommerce Information Retrieval has received little attention in the academic literature, yet it is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, and others). SIGIR has for several years seen sponsorship from these kinds of organisations, who clearly value the importance of research into Information Retrieval. This workshop brings together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, and to examine how to build a dataset for research into this fascinating topic. eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities (the kinds of search seen in when buying are different from those of web-page search (i.e. it is not clear how shopping and buying relate to the standard web-search interaction models)) through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings).
- URL
- go to publisher's site
- Other links
- Link to publication in Scopus
- Language
- English
- Persistent Identifier
- https://hdl.handle.net/11245.1/7a7f21fe-f3d4-4722-8f72-6dfbaa3156dd
- Downloads
-

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