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Social relations versus near neighbours: reliable recommenders in limited information social network collaborative filtering for online advertising

Published: 15 January 2020 Publication History

Abstract

Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.

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cover image ACM Conferences
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2019
1228 pages
ISBN:9781450368681
DOI:10.1145/3341161
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 the author(s) 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: 15 January 2020

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Author Tags

  1. collaborative filtering
  2. evaluation
  3. limited information
  4. near neighbours
  5. online advertising
  6. pearson correlation coefficient
  7. social networks

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
Overall Acceptance Rate 116 of 549 submissions, 21%

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  • (2022)A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domainsKnowledge-Based Systems10.1016/j.knosys.2022.109158251(109158)Online publication date: Sep-2022
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