short-paper

Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences

Authors:

Sultan Alanazi,

James Goulding,

Derek McAuleyAuthors Info & Claims

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

Pages 183 - 188

https://doi.org/10.1145/2914586.2914640

Published: 10 July 2016 Publication History

Abstract

It is increasingly rare to encounter a Web service that doesn't engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet several key issues still remain unresolved, including optimal handling of cold starts and how best to maintain user-privacy within that context. Recent work has demonstrated a potentially fruitful line of attack in the form of cross-system user modelling, which uses features generated from one domain to bootstrap recommendations in another. In this paper we evidence the effectiveness of this approach through direct real-world user feedback, deconstructing a cross-system news recommendation service where user models are generated via social media data. It is shown that even when a relatively naive vector-space approach is used, it is possible to automatically generate user-models that provide statistically superior performance than when items are explicitly filtered based on a user's self-declared preferences. Detailed qualitative analysis of why such effects occur indicate that different models are capturing widely different areas within a user's preference space, and that hybrid models represent fertile ground for future research.

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Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Katariya SBose JReddy MTappashetty SSharma A(2018)A Privacy Preserving Approach to Generating Personalized Recommendations Based on Short Text Classification2018 15th IEEE India Council International Conference (INDICON)10.1109/INDICON45594.2018.8987121(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/INDICON45594.2018.8987121
Khan MIbrahim RGhani I(2017)Cross Domain Recommender SystemsACM Computing Surveys10.1145/307356550:3(1-34)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3073565

Index Terms

Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
    2. Interaction paradigms
      1. Hypertext / hypermedia
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval tasks and goals
      1. Information extraction
      2. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Conferences

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

July 2016

354 pages

ISBN:9781450342476

DOI:10.1145/2914586

General Chairs:
Jamie Blustein
Dalhousie University, Canada
,
Eelco Herder
L3S Research Center, Germany
,
Program Chairs:
Jessica Rubart
OWL University of Applied Sciences, Germany
,
Helen Ashman
University of South Australia

Copyright © 2016 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 10 July 2016

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EPSRC

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HT '16

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SIGWEB

HT '16: 27th ACM Conference on Hypertext and Social Media

July 10 - 13, 2016

Nova Scotia, Halifax, Canada

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HT '16 Paper Acceptance Rate 16 of 54 submissions, 30%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Katariya SBose JReddy MTappashetty SSharma A(2018)A Privacy Preserving Approach to Generating Personalized Recommendations Based on Short Text Classification2018 15th IEEE India Council International Conference (INDICON)10.1109/INDICON45594.2018.8987121(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/INDICON45594.2018.8987121
Khan MIbrahim RGhani I(2017)Cross Domain Recommender SystemsACM Computing Surveys10.1145/307356550:3(1-34)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3073565

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