Ontology-based collaborative filtering recommendation algorithm

Zijian Zhang; Lin Gong; Jian Xie

doi:10.1007/978-3-642-38786-9_20

Ontology-based collaborative filtering recommendation algorithm

Zijian Zhang, Lin Gong, Jian Xie

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

E-learning system for knowledge points recommended primarily uses traditional collaborative filtering algorithm. Similarity calculation of knowledge points is often based on user rating above the intersection of knowledge points. The different semantic relations between knowledge points are not well considered, which results in the low recommended accuracy. This paper proposed an Ontology-based collaborative filtering recommendation algorithm, which could help users find the nearest neighbors even if the scores of knowledge points are little or zero. Through experiment, this algorithm was compared to traditional collaborative filtering recommendation algorithms. The new method achieved a better recommendation.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings
Pages	172-181
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-38786-9_20
Publication status	Published - 2013
Event	6th International Conference on Brain Inspired Cognitive Systems, BICS 2013 - Beijing, China Duration: 9 Jun 2013 → 11 Jun 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7888 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Brain Inspired Cognitive Systems, BICS 2013
Country/Territory	China
City	Beijing
Period	9/06/13 → 11/06/13

Keywords

collaborative filtering
ontology
semantic relations
similarity calculation of knowledge points

Access to Document

10.1007/978-3-642-38786-9_20

Cite this

Zhang, Z., Gong, L., & Xie, J. (2013). Ontology-based collaborative filtering recommendation algorithm. In Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings (pp. 172-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7888 LNAI). https://doi.org/10.1007/978-3-642-38786-9_20

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title = "Ontology-based collaborative filtering recommendation algorithm",

abstract = "E-learning system for knowledge points recommended primarily uses traditional collaborative filtering algorithm. Similarity calculation of knowledge points is often based on user rating above the intersection of knowledge points. The different semantic relations between knowledge points are not well considered, which results in the low recommended accuracy. This paper proposed an Ontology-based collaborative filtering recommendation algorithm, which could help users find the nearest neighbors even if the scores of knowledge points are little or zero. Through experiment, this algorithm was compared to traditional collaborative filtering recommendation algorithms. The new method achieved a better recommendation.",

keywords = "collaborative filtering, ontology, semantic relations, similarity calculation of knowledge points",

author = "Zijian Zhang and Lin Gong and Jian Xie",

year = "2013",

doi = "10.1007/978-3-642-38786-9_20",

language = "English",

isbn = "9783642387852",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "172--181",

booktitle = "Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings",

note = "6th International Conference on Brain Inspired Cognitive Systems, BICS 2013 ; Conference date: 09-06-2013 Through 11-06-2013",

}

Zhang, Z, Gong, L & Xie, J 2013, Ontology-based collaborative filtering recommendation algorithm. in Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7888 LNAI, pp. 172-181, 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, Beijing, China, 9/06/13. https://doi.org/10.1007/978-3-642-38786-9_20

Ontology-based collaborative filtering recommendation algorithm. / Zhang, Zijian; Gong, Lin; Xie, Jian.
Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings. 2013. p. 172-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7888 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xie, Jian

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N2 - E-learning system for knowledge points recommended primarily uses traditional collaborative filtering algorithm. Similarity calculation of knowledge points is often based on user rating above the intersection of knowledge points. The different semantic relations between knowledge points are not well considered, which results in the low recommended accuracy. This paper proposed an Ontology-based collaborative filtering recommendation algorithm, which could help users find the nearest neighbors even if the scores of knowledge points are little or zero. Through experiment, this algorithm was compared to traditional collaborative filtering recommendation algorithms. The new method achieved a better recommendation.

AB - E-learning system for knowledge points recommended primarily uses traditional collaborative filtering algorithm. Similarity calculation of knowledge points is often based on user rating above the intersection of knowledge points. The different semantic relations between knowledge points are not well considered, which results in the low recommended accuracy. This paper proposed an Ontology-based collaborative filtering recommendation algorithm, which could help users find the nearest neighbors even if the scores of knowledge points are little or zero. Through experiment, this algorithm was compared to traditional collaborative filtering recommendation algorithms. The new method achieved a better recommendation.

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KW - ontology

KW - semantic relations

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings

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Zhang Z, Gong L, Xie J. Ontology-based collaborative filtering recommendation algorithm. In Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings. 2013. p. 172-181. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-38786-9_20

Ontology-based collaborative filtering recommendation algorithm

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