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Social ranking: uncovering relevant content using tag-based recommender systems

Published: 23 October 2008 Publication History

Abstract

Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users' similarity based on their past tag activity. We infer tags' relationships based on their association to content. We then propose a mechanism to answer a user's query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.

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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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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Publication History

Published: 23 October 2008

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Author Tags

  1. recommender systems
  2. similarity
  3. tags
  4. web 2.0

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  • Research-article

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2022)Effective Graph Mining for Educational Data Mining and Interest RecommendationApplied Bionics and Biomechanics10.1155/2022/76101242022(1-5)Online publication date: 12-Aug-2022
  • (2021)Role-Aware Information Spread in Online Social NetworksEntropy10.3390/e2311154223:11(1542)Online publication date: 19-Nov-2021
  • (2021)Towards Contactless Learning Activities during Pandemics Using Autonomous Service RobotsApplied Sciences10.3390/app11211044911:21(10449)Online publication date: 7-Nov-2021
  • (2021)A Hybrid Recommender System Using KNN and ClusteringInternational Journal of Information Technology & Decision Making10.1142/S021962202150005X20:02(553-596)Online publication date: 31-Mar-2021
  • (2021)Mathematics of Trust: Controlled and Uncontrolled Influence in Social Systems2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534172(1-8)Online publication date: 18-Jul-2021
  • (2021)A Tag-based Recommender System for Regression Test Case Prioritization2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)10.1109/ICSTW52544.2021.00035(146-157)Online publication date: Apr-2021
  • (2020)Spatio-Temporal Patterns of Information DiffusionModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_3(15-25)Online publication date: 17-Mar-2020
  • (2019)Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationshipsIntelligent Data Analysis10.3233/IDA-18391023:3(623-639)Online publication date: 28-Apr-2019
  • (2019)Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative FilteringIEEE Transactions on Cybernetics10.1109/TCYB.2018.284192449:7(2678-2692)Online publication date: Jul-2019
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