research-article

The wisdom of the few: a collaborative filtering approach based on expert opinions from the web

Authors:

Xavier Amatriain,

Josep M. Pujol,

Nuria OliverAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 532 - 539

https://doi.org/10.1145/1571941.1572033

Published: 19 July 2009 Publication History

Abstract

Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.

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Cited By

Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002
Murphy CCarew PStapleton L(2024)Human-Centred Machine Learning Approaches for Smart Cultural Heritage Spaces: A Multicontinental ReviewIFAC-PapersOnLine10.1016/j.ifacol.2024.07.17158:3(322-327)Online publication date: 2024
https://doi.org/10.1016/j.ifacol.2024.07.171
Qi ZHuang YWu JLi S(2023)FoodS and FoodIM: Food-Testing Item Recommendation Models for Two Different Users with Different Usage AbilitiesProceedings of the 2023 4th Asia Service Sciences and Software Engineering Conference10.1145/3634814.3634815(1-9)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1145/3634814.3634815
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Index Terms

The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2009 ACM.

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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Published: 19 July 2009

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002
Murphy CCarew PStapleton L(2024)Human-Centred Machine Learning Approaches for Smart Cultural Heritage Spaces: A Multicontinental ReviewIFAC-PapersOnLine10.1016/j.ifacol.2024.07.17158:3(322-327)Online publication date: 2024
https://doi.org/10.1016/j.ifacol.2024.07.171
Qi ZHuang YWu JLi S(2023)FoodS and FoodIM: Food-Testing Item Recommendation Models for Two Different Users with Different Usage AbilitiesProceedings of the 2023 4th Asia Service Sciences and Software Engineering Conference10.1145/3634814.3634815(1-9)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1145/3634814.3634815
Chang TChen Y(2022)Creating a System of IOE-PDPTA to Bridge Tourists and Poster Designers: An Application of IOE in Personalized Poster DesignSystems10.3390/systems1004012510:4(125)Online publication date: 19-Aug-2022
https://doi.org/10.3390/systems10040125
Rajapakse DLeith D(2022)Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree SearchProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546786(350-359)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546786
Faruk KRahman AShusmita SAwlad MDas PMehedi MIqbal SRasel A(2022)K Nearest Neighbour Collaborative Filtering for Expertise Recommendation SystemsDistributed Computing and Artificial Intelligence, 19th International Conference10.1007/978-3-031-20859-1_19(187-196)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-20859-1_19
Lechiakh MMaurer A(2022)FEBR: Expert-Based Recommendation Framework for Beneficial and Personalized ContentNetworked Systems10.1007/978-3-031-17436-0_5(52-68)Online publication date: 28-Sep-2022
https://doi.org/10.1007/978-3-031-17436-0_5
Aygoren OKoch S(2021)Community Support or Funding Amount: Actual Contribution of Reward-Based Crowdfunding to Market Success of Video Game Projects on KickstarterSustainability10.3390/su1316919513:16(9195)Online publication date: 16-Aug-2021
https://doi.org/10.3390/su13169195
Shams SAnderson DLeith DDiaz FShah CSuel TCastells PJones RSakai T(2021)Cluster-Based Bandits: Fast Cold-Start for Recommender System New UsersProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463033(1613-1616)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463033
Lumauag R(2021)A Modified Memory-Based Collaborative Filtering Algorithm based on a New User Similarity Measure2021 Second International Conference on Innovative Technology Convergence (CITC)10.1109/CITC54365.2021.00020(69-73)Online publication date: Dec-2021
https://doi.org/10.1109/CITC54365.2021.00020
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