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Connecting sellers and buyers on the world's largest inventory

Published: 27 September 2018 Publication History

Abstract

At eBay, sellers can offer virtually any type of listing, rendering the world's largest inventory, with well over a billion items. Yet, the noisy nature of the input data and the extremely long-tailed item distribution pose a variety of challenges for search and recommendation, such as understanding the unique attributes (aspects) of the products, their importance to both sellers and buyers, and their intra-relationships, all essential to providing a high-quality user experience on the site.
In this talk, I will present several challenges and corresponding solution frameworks recently developed at eBay Research for aspect extraction, normalization, weighting, and relation inference; the mapping of relationships between e-commerce entities for matching uploaded listings to catalog products and feeding the e-commerce knowledge graph; the recommendation of categories for sellers' contributions; and the automatic generation of textual fields (title, description) to bridge the gap between sellers and buyers by helping them speak the same language. Our methods combine a variety of language processing and computer vision approaches applied on the different types of data contributed by sellers. Learning to rank, named entity recognition, object identification, machine translation, and summarization are just a few example techniques that come to play. Our methods drive different usage scenarios by enabling a better representation of users and items and an effective computation of their similarities. I will also describe how our applied research teams perform their work, from the development of initial prototypes, through offline and online production processes, to different evaluation schemes. I will conclude the talk by reviewing open challenges in large-scale e-commerce that will have to be addressed in the years to come.

References

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2012. RecSys '12: Proceedings of the Sixth ACM Conference on Recommender Systems. ACM, New York, NY, USA.
[2]
Ido Guy. 2015. Social recommender systems. In Recommender Systems Handbook. Springer, 511--543.
[3]
Ido Guy. 2018. The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries. ACM Trans. Inf. Syst. 36, 3, Article 30 (2018), 28 pages.
[4]
Ido Guy. 2018. People recommendation on social media. In Social Information Access. Springer, Cham, 570--623.
[5]
Ido Guy, Alejandro Jaimes, Pau Agulló, Pat Moore, Palash Nandy, Chahab Nastar, and Henrik Schinzel. 2010. Will Recommenders Kill Search?: Recommender Systems - an Industry Perspective. In Proc. of RecSys. 7--12.
[6]
Ido Guy, Roy Levin, Tal Daniel, and Ella Bolshinsky. 2015. Islands in the Stream: A Study of Item Recommendation Within an Enterprise Social Stream. In Proc. of SIGIR. 665--674.
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Ido Guy, Victor Makarenkov, Niva Hazon, Lior Rokach, and Bracha Shapira. 2018. Identifying Informational vs. Conversational Questions on Community Question Answering Archives. In Proc. of WSDM. 216--224.
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Ido Guy, Inbal Ronen, Elad Kravi, and Maya Barnea. 2016. Increasing Activity in Enterprise Online Communities Using Content Recommendation. ACM Trans. Comput.-Hum. Interact. 23, 4 (2016), 22:1--22:28.
[9]
Ido Guy, Inbal Ronen, Naama Zwerdling, Irena Zuyev-Grabovitch, and Michal Jacovi. 2016. What is Your Organization 'Like'?: A Study of Liking Activity in the Enterprise. In Proc. of CHI. 3025--3037.
[10]
Ido Guy and Bracha Shapira. 2018. From Royals to Vegans: Characterizing Question Trolling on a Community Question Answering Website. In Proc. of SIGIR. 835--844.
[11]
Ido Guy, Tal Steier, Maya Barnea, Inbal Ronen, and Tal Daniel. 2012. Swimming Against the Streamz: Search and Analytics over the Enterprise Activity Stream. In Proc. CIKM. 1587--1591.
[12]
Michal Jacovi, Ido Guy, Shiri Kremer-Davidson, Sara Porat, and Netta Aizenbud-Reshef. 2014. The Perception of Others: Inferring Reputation from Social Media in the Enterprise. In Proc. of CSCW. 756--766.
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Or Levi, Ido Guy, Fiana Raiber, and Oren Kurland. 2018. Selective Cluster Presentation on the Search Results Page. ACM Trans. Inf. Syst. 36, 3, Article 28 (2018), 42 pages.
[14]
Inbal Ronen, Ido Guy, Elad Kravi, and Maya Barnea. 2014. Recommending Social Media Content to Community Owners. In Proc. of SIGIR (SIGIR '14). 243--252.

Cited By

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  • (2020)E-Commerce Dispute Resolution PredictionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411906(1465-1474)Online publication date: 19-Oct-2020

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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Author Tags

  1. E-commerce
  2. electronic commerce
  3. machine learning
  4. structured data
  5. text processing

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2020)E-Commerce Dispute Resolution PredictionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411906(1465-1474)Online publication date: 19-Oct-2020

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