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Extending a hybrid tag-based recommender system with personalization

Authors:

Frederico Durao,

Peter DologAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 1723 - 1727

https://doi.org/10.1145/1774088.1774457

Published: 22 March 2010 Publication History

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Abstract

Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. This paper proposes a semantic extension for a hybrid tag-based recommender system, which suggests similar Web pages based on the similarity of their tags. The semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. With the goal of generating more semantically grounded recommendations, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. In order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people from 12 countries using data from del.icio.us.

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Kanika Chakraverty SChakraborty PAgnihotri SMohapatra SBansal P(2019)KELDECProceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval10.1145/3342827.3342849(99-103)Online publication date: 28-Jun-2019
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Index Terms

Extending a hybrid tag-based recommender system with personalization
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Published In

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

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: 22 March 2010

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SAC'10: The 2010 ACM Symposium on Applied Computing

March 22 - 26, 2010

Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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https://doi.org/10.1109/FUZZ-IEEE60900.2024.10612086
Azizi M(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
https://doi.org/10.1109/ICSTW52544.2021.00035
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https://dl.acm.org/doi/10.1145/3342827.3342849
Vélez-Langs OCaicedo-Castro I(2019)Recommender Systems for an Enhanced Mobile e-LearningHCI International 2019 – Late Breaking Papers10.1007/978-3-030-30033-3_28(357-365)Online publication date: 31-Aug-2019
https://doi.org/10.1007/978-3-030-30033-3_28
Kim HKim DJo J(2018)A unified music recommender system using listening habits and semantics of tagsInternational Journal of Intelligent Information and Database Systems10.1504/IJIIDS.2014.0604608:1(14-30)Online publication date: 14-Dec-2018
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Sruthi MPulari SGowtham R(2018)Comprehensive Study on Usage of Multi Objectives in Recommender SystemsComputational Vision and Bio Inspired Computing10.1007/978-3-319-71767-8_5(55-67)Online publication date: 20-Feb-2018
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Gedikli FJannach D(2018)Recommender Systems, Semantic-BasedEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_116(2137-2147)Online publication date: 12-Jun-2018
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Vairavasundaram SVaradharajan VVairavasundaram IRavi L(2018)Data mining-based tag recommendation systemWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11495:3(87-112)Online publication date: 14-Dec-2018
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Benhamdi SBabouri AChiky R(2017)Personalized recommender system for e-Learning environmentEducation and Information Technologies10.1007/s10639-016-9504-y22:4(1455-1477)Online publication date: 1-Jul-2017
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Recommendations

A framework for tag-aware recommender systems

Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain

Personalization in tag ontology learning for recommendation making

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