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Mining semantic data for solving first-rater and cold-start problems in recommender systems

Published: 21 September 2011 Publication History

Abstract

Recommender systems are becoming very popular in recent years, mainly in the e-commerce sites, although they are increasing in importance in other areas such as e-learning, tourism, news pages, etc. These systems are endowed with intelligent mechanisms to personalize recommendations about products or services. However, they present some serious drawbacks that impact in user satisfaction. First-rater and cold-start problems are two important drawbacks that take place respectively when new products or new users are introduced in the system. The lack of rating about these products or from these users prevents from making recommendations. Nowadays, traditional collaborative filtering methods have being replaced by web mining techniques in order to deal with scalability and performance problems, but first-rater and cold-start ones require a different strategy. In this work, we propose a methodology that combines data mining techniques with semantic data in order to overcome these two important shortcomings.

References

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Huang, Y. and Bian, L. 2009. A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attraction over the Internet, Expert Systems with Applications, 36, 933--943.
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Lee, CH., Kim, Y. H., Rhee, P. K. 2001. Web personalization expert with combining collaborative filtering and association rule Mining Technique. Expert Systems with Applications, 21, 131--137.
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Moreno M. N., Pinho, J., López, V, y Polo, M. J. 2010. Multivariate Discretization for Associative Classification in a Sparse Data Application Domain., Lecture Notes in Artificial Intelligence, v. 6076, Springer 104--111.
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Cited By

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  • (2021)Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start ProblemJournal of Data and Information Quality10.1145/342925113:3(1-27)Online publication date: 27-Apr-2021
  • (2015)A Multi-Agent Brokerage Platform for Media Content RecommendationInternational Journal of Applied Mathematics and Computer Science10.1515/amcs-2015-003825:3(513-527)Online publication date: 1-Sep-2015

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  1. Mining semantic data for solving first-rater and cold-start problems in recommender systems

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    cover image ACM Other conferences
    IDEAS '11: Proceedings of the 15th Symposium on International Database Engineering & Applications
    September 2011
    274 pages
    ISBN:9781450306270
    DOI:10.1145/2076623
    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

    New York, NY, United States

    Publication History

    Published: 21 September 2011

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    Author Tags

    1. associative classification
    2. cold-start
    3. first-rater
    4. recommender systems
    5. semantic web mining

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    View all
    • (2021)Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start ProblemJournal of Data and Information Quality10.1145/342925113:3(1-27)Online publication date: 27-Apr-2021
    • (2015)A Multi-Agent Brokerage Platform for Media Content RecommendationInternational Journal of Applied Mathematics and Computer Science10.1515/amcs-2015-003825:3(513-527)Online publication date: 1-Sep-2015

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