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Integrating tags in a semantic content-based recommender

Published: 23 October 2008 Publication History

Abstract

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags.
In this paper, we investigate whether folksonomies might be a valuable source of information about user interests. The main contribution is a strategy that enables a content-based recommender to infer user interests by applying machine learning techniques both on the "official" item descriptions provided by a publisher, and on tags which users adopt to freely annotate relevant items. Static content and tags are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests often hidden behind keywords. The proposed approach has been evaluated in the context of cultural heritage personalization. Preliminary experiments involving 30 real users show an improvement in the predictive accuracy of the tag-augmented recommender compared to the pure content-based one.

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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. collaborative tagging
  2. machine learning
  3. recommender systems
  4. word sense disambiguation

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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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  • (2024)Uncertainty-aware graph neural network for semi-supervised diversified recommendationSocial Network Analysis and Mining10.1007/s13278-024-01242-914:1Online publication date: 17-Apr-2024
  • (2023)A Comprehensive Survey of Recommender Systems Based on Deep LearningApplied Sciences10.3390/app13201137813:20(11378)Online publication date: 17-Oct-2023
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  • (2023)Content-Based Recommender Systems TaxonomyFoundations of Computing and Decision Sciences10.2478/fcds-2023-000948:2(211-241)Online publication date: 30-Jun-2023
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