research-article

Improving recommendation accuracy based on item-specific tag preferences

Authors:

Dietmar JannachAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 4, Issue 1

Article No.: 11, Pages 1 - 19

https://doi.org/10.1145/2414425.2414436

Published: 01 February 2013 Publication History

Abstract

In recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about the recommendable items. Recent research has indicated that “attaching feelings to tags” is experienced by users as a valuable means to express which features of an item they particularly like or dislike. When following such an approach, users would therefore not only add tags to an item as in usual Web 2.0 applications, but also attach a preference (affect) to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. In this work, we show how this additional preference data can be exploited by a recommender system to make more accurate predictions.

In contrast to previous work, which also relied on so-called tag preferences to enhance the predictive accuracy of recommender systems, we argue that tag preferences should be considered in the context of an item. We therefore propose new schemes to infer and exploit context-specific tag preferences in the recommendation process. An evaluation on two different datasets reveals that our approach is capable of providing more accurate recommendations than previous tag-based recommender algorithms and recent tag-agnostic matrix factorization techniques.

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Index Terms

Improving recommendation accuracy based on item-specific tag preferences
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. World Wide Web

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 4, Issue 1

Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

January 2013

357 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2414425

Issue’s Table of Contents

Copyright © 2013 ACM.

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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Association for Computing Machinery

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Publication History

Published: 01 February 2013

Received: 01 August 2010

Accepted: 01 July 2010

Revised: 01 March 2010

Published in TIST Volume 4, Issue 1

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Cited By

Wu ZDai XWang XXiong YGao SLiu D(2023)A Multi-Label Recommendation Algorithm Based on Graph Attention and Sentiment Correction2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)10.1109/AINIT59027.2023.10212701(396-401)Online publication date: 16-Jun-2023
https://doi.org/10.1109/AINIT59027.2023.10212701
Huang SHuang XZeng TCai DZhu D(2022)A Long Command Subsequence Algorithm for Manufacturing Industry Recommendation System with Similarity Connection TechnologyInternational Journal of Mathematical Models and Methods in Applied Sciences10.46300/9101.2022.16.1916(112-118)Online publication date: 17-May-2022
https://doi.org/10.46300/9101.2022.16.19
Huang SHuang XZeng TCai DZhu D(2022)A long command subsequence algorithm for manufacturing industry recommendation systems with similarity connection technologyApplied Mathematics and Nonlinear Sciences10.2478/amns.2021.2.002328:2(789-798)Online publication date: 30-Sep-2022
https://doi.org/10.2478/amns.2021.2.00232
Piçarra NReis EChambel TArriaga P(2022)Searching, Navigating, and Recommending Movies through Emotions: A Scoping ReviewHuman Behavior and Emerging Technologies10.1155/2022/78310132022(1-24)Online publication date: 2-Dec-2022
https://doi.org/10.1155/2022/7831013
Elahi MBakhshandegan Moghaddam FHosseini RRimaz MEl Ioini NTkalcic MTrattner CTillo T(2021)Recommending Videos in Cold Start With Automatic Visual TagsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461687(54-60)Online publication date: 21-Jun-2021
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Pérez-Almaguer YYera RAlzahrani AMartínez L(2021)Content-based group recommender systems: a general taxonomy and further improvementsExpert Systems with Applications10.1016/j.eswa.2021.115444(115444)Online publication date: Jun-2021
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Nguyen QDuong-Trung NLe Ha DHa XPhan TPham HHuynh H(2020)Movie Recommender Systems Made Through Tag InterpolationProceedings of the 4th International Conference on Machine Learning and Soft Computing10.1145/3380688.3380712(154-158)Online publication date: 17-Jan-2020
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Shanmuga Sundari PSubaji M(2020)Integrating Sentiment Analysis on hybrid Collaborative Filtering Method in a Big Data EnvironmentInternational Journal of Information Technology & Decision Making10.1142/S0219622020500108Online publication date: 19-Feb-2020
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Rimaz MHosseini RElahi MMoghaddam F(2020)AudioLens: Audio-Aware Video Recommendation for Mitigating New Item ProblemService-Oriented Computing – ICSOC 2020 Workshops10.1007/978-3-030-76352-7_35(365-378)Online publication date: 14-Dec-2020
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