A research paper recommender system using a dynamic normalized tree of concepts model for user modelling

Modhi Al Alshaikh; Gulden Uchyigit; Roger Evans

A research paper recommender system using a dynamic normalized tree of concepts model for user modelling

Modhi Al Alshaikh, Gulden Uchyigit, Roger Evans

University of Brighton

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

Original language	English
Title of host publication	RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science
Place of Publication	Brighton
Publisher	IEEE
Pages	0-0
Number of pages	1
ISBN (Print)	9781509054763
Publication status	Published - 10 May 2017
Event	RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science - Brighton, 10-12 May 2017 Duration: 10 May 2017 → …

Conference

Conference	RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science
Period	10/05/17 → …

Bibliographical note

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

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Cite this

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title = "A research paper recommender system using a dynamic normalized tree of concepts model for user modelling",

abstract = "The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.",

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Al Alshaikh, M, Uchyigit, G & Evans, R 2017, A research paper recommender system using a dynamic normalized tree of concepts model for user modelling. in RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science. IEEE, Brighton, pp. 0-0, RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science, 10/05/17.

A research paper recommender system using a dynamic normalized tree of concepts model for user modelling. / Al Alshaikh, Modhi; Uchyigit, Gulden; Evans, Roger.
RCIS 2017: IEEE 11th International Conference on Research Challenges in Information Science. Brighton: IEEE, 2017. p. 0-0.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

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N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

PY - 2017/5/10

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N2 - The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

AB - The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

M3 - Conference contribution with ISSN or ISBN

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ER -

A research paper recommender system using a dynamic normalized tree of concepts model for user modelling

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