Improvements to Collaborative Filtering Systems

Fu Lee Wang

doi:10.1007/978-3-540-30497-5_150

Improvements to Collaborative Filtering Systems

Fu Lee Wang

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Recommender systems make suggestions to users. Collaborative filtering techniques make the predictions by using the ratings on items of other users. In this paper, we have studied item-based and user-based collaborative filtering techniques. We identify the shortcomings of current filtering techniques. The performance of recommender systems was deeply affected by user's rating behavior. We propose some improvements to overcome this limitation. User evaluation has been conducted. Experiment results show that the new algorithms improve the performance of recommender systems significantly.

Original language	English
Pages (from-to)	975-981
Number of pages	7
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3314
DOIs	https://doi.org/10.1007/978-3-540-30497-5_150
Publication status	Published - 2004
Externally published	Yes

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10.1007/978-3-540-30497-5_150

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title = "Improvements to Collaborative Filtering Systems",

abstract = "Recommender systems make suggestions to users. Collaborative filtering techniques make the predictions by using the ratings on items of other users. In this paper, we have studied item-based and user-based collaborative filtering techniques. We identify the shortcomings of current filtering techniques. The performance of recommender systems was deeply affected by user's rating behavior. We propose some improvements to overcome this limitation. User evaluation has been conducted. Experiment results show that the new algorithms improve the performance of recommender systems significantly.",

author = "Wang, {Fu Lee}",

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language = "English",

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AU - Wang, Fu Lee

PY - 2004

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N2 - Recommender systems make suggestions to users. Collaborative filtering techniques make the predictions by using the ratings on items of other users. In this paper, we have studied item-based and user-based collaborative filtering techniques. We identify the shortcomings of current filtering techniques. The performance of recommender systems was deeply affected by user's rating behavior. We propose some improvements to overcome this limitation. User evaluation has been conducted. Experiment results show that the new algorithms improve the performance of recommender systems significantly.

AB - Recommender systems make suggestions to users. Collaborative filtering techniques make the predictions by using the ratings on items of other users. In this paper, we have studied item-based and user-based collaborative filtering techniques. We identify the shortcomings of current filtering techniques. The performance of recommender systems was deeply affected by user's rating behavior. We propose some improvements to overcome this limitation. User evaluation has been conducted. Experiment results show that the new algorithms improve the performance of recommender systems significantly.

UR - http://www.scopus.com/inward/record.url?scp=35048864410&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-30497-5_150

DO - 10.1007/978-3-540-30497-5_150

M3 - Article

AN - SCOPUS:35048864410

SN - 0302-9743

VL - 3314

SP - 975

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

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

ER -

Improvements to Collaborative Filtering Systems

Abstract

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