Hybrid recommendation approaches: Collaborative filtering via valuable content information

Ya Yueh Shih; Duen-Ren Liu

doi:10.1109/HICSS.2005.302

Hybrid recommendation approaches: Collaborative filtering via valuable content information

Ya Yueh Shih, Duen-Ren Liu

Institute of Information Management

Research output: Contribution to journal › Conference article › peer-review

42 Scopus citations

Abstract

Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.

Original language	English
Number of pages	1
Journal	Proceedings of the Annual Hawaii International Conference on System Sciences
DOIs	https://doi.org/10.1109/HICSS.2005.302
State	Published - 10 Nov 2005
Event	38th Annual Hawaii International Conference on System Sciences - Big Island, HI, United States Duration: 3 Jan 2005 → 6 Jan 2005

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10.1109/HICSS.2005.302

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title = "Hybrid recommendation approaches: Collaborative filtering via valuable content information",

abstract = "Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.",

author = "Shih, {Ya Yueh} and Duen-Ren Liu",

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AU - Liu, Duen-Ren

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N2 - Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.

AB - Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.

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M3 - Conference article

AN - SCOPUS:27544474814

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JO - Proceedings of the Annual Hawaii International Conference on System Sciences

JF - Proceedings of the Annual Hawaii International Conference on System Sciences

Y2 - 3 January 2005 through 6 January 2005

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Hybrid recommendation approaches: Collaborative filtering via valuable content information

Abstract

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