An effective threshold-based neighbor selection in collaborative filtering

Tack Hun Kim; Sung Bong Yang

doi:10.1007/978-3-540-71496-5_75

An effective threshold-based neighbor selection in collaborative filtering

Tack Hun Kim, Sung Bong Yang

Department of Computer Science

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

9 Citations (Scopus)

Abstract

In this paper we present a recommender system using an effective threshold-based neighbor selection in collaborative filtering. The proposed method uses the substitute neighbors of the test customer who may have an unusual preferences or who are the first rater. The experimental results show that the recommender systems using the proposed method find the proper neighbors and give a good prediction quality.

Original language	English
Title of host publication	Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings
Publisher	Springer Verlag
Pages	712-715
Number of pages	4
ISBN (Print)	3540714944, 9783540714941
DOIs	https://doi.org/10.1007/978-3-540-71496-5_75
Publication status	Published - 2007
Event	29th European Conference on IR Research, ECIR 2007 - Rome, Italy Duration: 2007 Apr 2 → 2007 Apr 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4425 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	29th European Conference on IR Research, ECIR 2007
Country	Italy
City	Rome
Period	07/4/2 → 07/4/5

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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Kim, T. H., & Yang, S. B. (2007). An effective threshold-based neighbor selection in collaborative filtering. In Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings (pp. 712-715). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4425 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-71496-5_75

Kim, Tack Hun ; Yang, Sung Bong. / An effective threshold-based neighbor selection in collaborative filtering. Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings. Springer Verlag, 2007. pp. 712-715 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An effective threshold-based neighbor selection in collaborative filtering",

abstract = "In this paper we present a recommender system using an effective threshold-based neighbor selection in collaborative filtering. The proposed method uses the substitute neighbors of the test customer who may have an unusual preferences or who are the first rater. The experimental results show that the recommender systems using the proposed method find the proper neighbors and give a good prediction quality.",

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Kim, TH & Yang, SB 2007, An effective threshold-based neighbor selection in collaborative filtering. in Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4425 LNCS, Springer Verlag, pp. 712-715, 29th European Conference on IR Research, ECIR 2007, Rome, Italy, 07/4/2. https://doi.org/10.1007/978-3-540-71496-5_75

An effective threshold-based neighbor selection in collaborative filtering. / Kim, Tack Hun; Yang, Sung Bong.

Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings. Springer Verlag, 2007. p. 712-715 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4425 LNCS).

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

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AB - In this paper we present a recommender system using an effective threshold-based neighbor selection in collaborative filtering. The proposed method uses the substitute neighbors of the test customer who may have an unusual preferences or who are the first rater. The experimental results show that the recommender systems using the proposed method find the proper neighbors and give a good prediction quality.

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Kim TH, Yang SB. An effective threshold-based neighbor selection in collaborative filtering. In Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings. Springer Verlag. 2007. p. 712-715. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-71496-5_75

An effective threshold-based neighbor selection in collaborative filtering

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