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Refining preference-based search results through Bayesian filtering

Published: 28 January 2007 Publication History

Abstract

Preference-based search (PBS) is a popular approach for helping consumers find their desired items from online catalogs. Currently most PBS tools generate search results by a certain set of criteria based on preferences elicited from the current user during the interaction session. Due to the incompleteness and uncertainty of the user's preferences, the search results are often inaccurate and may contain items that the user has no desire to select. In this paper we develop an efficient Bayesian filter based on a group of users' past choice behavior and use it to refine the search results by filtering out items which are unlikely to be selected by the user. Our preliminary experiment shows that our approach is highly promising in generating more accurate search results and saving user's interaction effort.

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Cited By

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  • (2024)Data Indexing and Filtering Techniques for Big Data SystemsResource Management on Distributed Systems10.1002/9781119912965.ch9(201-235)Online publication date: 6-Sep-2024
  • (2023)Modeling Tradeoffs Using Preference-Based Feedback in Session-Based Recommender SystemsIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32148014:3(511-521)Online publication date: Jun-2023
  • (2021)A Parallel Processing Technique for Extracting and Storing User Specified Data2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)10.1109/FiCloud49777.2021.00042(241-249)Online publication date: Aug-2021
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cover image ACM Conferences
IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces
January 2007
388 pages
ISBN:1595934812
DOI:10.1145/1216295
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 January 2007

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Author Tags

  1. Bayesian filtering
  2. interaction effort
  3. preference-based search

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

View all
  • (2024)Data Indexing and Filtering Techniques for Big Data SystemsResource Management on Distributed Systems10.1002/9781119912965.ch9(201-235)Online publication date: 6-Sep-2024
  • (2023)Modeling Tradeoffs Using Preference-Based Feedback in Session-Based Recommender SystemsIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32148014:3(511-521)Online publication date: Jun-2023
  • (2021)A Parallel Processing Technique for Extracting and Storing User Specified Data2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)10.1109/FiCloud49777.2021.00042(241-249)Online publication date: Aug-2021
  • (2020)Filtering and Storing User Preferred Data: an Apache Spark Based Approach2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00115(673-679)Online publication date: Aug-2020
  • (2010)Experiments on the preference-based organization interface in recommender systemsACM Transactions on Computer-Human Interaction10.1145/1721831.172183617:1(1-33)Online publication date: 6-Apr-2010
  • (2009)An Implementation of the CORDRA Architecture Enhanced for Systematic Reuse of Learning ObjectsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2008.13021:6(925-938)Online publication date: 1-Jun-2009

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