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Buzz-based recommender system

Published: 20 April 2009 Publication History

Abstract

In this paper, we describe a buzz-based recommender system based on a large source of queries in an eCommerce application. The system detects bursts in query trends. These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries. The system follows the paradigm of limited quantity merchandising, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity, and improving activity and stickiness on the site. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood.

References

[1]
Parikh N., Sundaresan N. Scalable and near real-time burst detection from eCommerce queries. KDD 2008. 972--980.
[2]
Parikh N., Sundaresan N. Inferring Semantic Query Relations from Collective User Behavior. CIKM 2008, Napa, CA, October 2008.

Cited By

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  • (2020)A Big Data Semantic Driven Context Aware Recommendation MethodIntelligent and Fuzzy Techniques: Smart and Innovative Solutions10.1007/978-3-030-51156-2_103(894-902)Online publication date: 11-Jul-2020
  • (2019)A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer ItemsIEEE Access10.1109/ACCESS.2019.29578817(182664-182678)Online publication date: 2019
  • (2018)Integrating Traditional Stores and e-Commerce into a Multi-tiered Recommender System Architecture Supported by IoTInternet and Distributed Computing Systems10.1007/978-3-319-97795-9_5(50-62)Online publication date: 24-Jul-2018
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cover image ACM Conferences
WWW '09: Proceedings of the 18th international conference on World wide web
April 2009
1280 pages
ISBN:9781605584874
DOI:10.1145/1526709

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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Author Tags

  1. buzz
  2. novelty
  3. recommenders
  4. serendipity

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2020)A Big Data Semantic Driven Context Aware Recommendation MethodIntelligent and Fuzzy Techniques: Smart and Innovative Solutions10.1007/978-3-030-51156-2_103(894-902)Online publication date: 11-Jul-2020
  • (2019)A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer ItemsIEEE Access10.1109/ACCESS.2019.29578817(182664-182678)Online publication date: 2019
  • (2018)Integrating Traditional Stores and e-Commerce into a Multi-tiered Recommender System Architecture Supported by IoTInternet and Distributed Computing Systems10.1007/978-3-319-97795-9_5(50-62)Online publication date: 24-Jul-2018
  • (2016)A distributed and multi-tiered software architecture for assessing e-Commerce recommendationsConcurrency and Computation: Practice & Experience10.1002/cpe.379828:18(4507-4531)Online publication date: 25-Dec-2016
  • (2015)Predicting customer purchase behavior in the e-commerce contextElectronic Commerce Research10.1007/s10660-015-9191-615:4(427-452)Online publication date: 1-Dec-2015
  • (2013)A Multi-tiered Recommender System Architecture for Supporting E-CommerceIntelligent Distributed Computing VI10.1007/978-3-642-32524-3_10(71-81)Online publication date: 2013
  • (2012)Early detection of buzzwords based on large-scale time-series analysis of blog entriesProceedings of the 23rd ACM conference on Hypertext and social media10.1145/2309996.2310042(275-284)Online publication date: 25-Jun-2012
  • (2012)Rewriting null e-commerce queries to recommend productsProceedings of the 21st International Conference on World Wide Web10.1145/2187980.2187989(73-82)Online publication date: 16-Apr-2012
  • (2012)A multi-agent recommender system for supporting device adaptivity in e-CommerceJournal of Intelligent Information Systems10.1007/s10844-011-0160-938:2(393-418)Online publication date: 1-Apr-2012
  • (2011)User behavior in zero-recall ecommerce queriesProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2009930(75-84)Online publication date: 24-Jul-2011
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