Collaborative filtering recommendation algorithm based on user ratings and item attributes

Jiaming Zhang; Kaoru Hirota; Yaping Dai; Zheng Meng

Collaborative filtering recommendation algorithm based on user ratings and item attributes

Jiaming Zhang, Kaoru Hirota^*, Yaping Dai, Zheng Meng

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

A collaborative filtering recommendation algorithm is proposed in recommender systems based on user ratings and item attributes to relieve the problem of data sparsity, which introduces the information of item attributes and considers the similarity of item attributes preference of users as well as the similarity of user ratings. It improves the accuracy of recommendation under the influence of data sparsity compared to the traditional collaborative filtering (CF) algorithm considering user ratings only. The experimental results of the proposed algorithm based on MovieLens data set are compared with the traditional CF algorithm and it is found that the mean absolute error (MAE) reduces by 0.08. The proposal improves the recommendation accuracy in recommendation of the commodity and social network and gives more satisfactory recommendation to users.

Original language	English
Publication status	Published - 2017
Event	5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017 - Beijing, China Duration: 2 Nov 2017 → 5 Nov 2017

Conference

Conference	5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017
Country/Territory	China
City	Beijing
Period	2/11/17 → 5/11/17

Keywords

Collaborative Filtering
Item Attribute
Recommendation Algorithm
Similarity
User Rating

Cite this

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title = "Collaborative filtering recommendation algorithm based on user ratings and item attributes",

abstract = "A collaborative filtering recommendation algorithm is proposed in recommender systems based on user ratings and item attributes to relieve the problem of data sparsity, which introduces the information of item attributes and considers the similarity of item attributes preference of users as well as the similarity of user ratings. It improves the accuracy of recommendation under the influence of data sparsity compared to the traditional collaborative filtering (CF) algorithm considering user ratings only. The experimental results of the proposed algorithm based on MovieLens data set are compared with the traditional CF algorithm and it is found that the mean absolute error (MAE) reduces by 0.08. The proposal improves the recommendation accuracy in recommendation of the commodity and social network and gives more satisfactory recommendation to users.",

keywords = "Collaborative Filtering, Item Attribute, Recommendation Algorithm, Similarity, User Rating",

author = "Jiaming Zhang and Kaoru Hirota and Yaping Dai and Zheng Meng",

year = "2017",

language = "English",

note = "5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017 ; Conference date: 02-11-2017 Through 05-11-2017",

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TY - CONF

T1 - Collaborative filtering recommendation algorithm based on user ratings and item attributes

AU - Zhang, Jiaming

AU - Hirota, Kaoru

AU - Dai, Yaping

AU - Meng, Zheng

PY - 2017

Y1 - 2017

N2 - A collaborative filtering recommendation algorithm is proposed in recommender systems based on user ratings and item attributes to relieve the problem of data sparsity, which introduces the information of item attributes and considers the similarity of item attributes preference of users as well as the similarity of user ratings. It improves the accuracy of recommendation under the influence of data sparsity compared to the traditional collaborative filtering (CF) algorithm considering user ratings only. The experimental results of the proposed algorithm based on MovieLens data set are compared with the traditional CF algorithm and it is found that the mean absolute error (MAE) reduces by 0.08. The proposal improves the recommendation accuracy in recommendation of the commodity and social network and gives more satisfactory recommendation to users.

AB - A collaborative filtering recommendation algorithm is proposed in recommender systems based on user ratings and item attributes to relieve the problem of data sparsity, which introduces the information of item attributes and considers the similarity of item attributes preference of users as well as the similarity of user ratings. It improves the accuracy of recommendation under the influence of data sparsity compared to the traditional collaborative filtering (CF) algorithm considering user ratings only. The experimental results of the proposed algorithm based on MovieLens data set are compared with the traditional CF algorithm and it is found that the mean absolute error (MAE) reduces by 0.08. The proposal improves the recommendation accuracy in recommendation of the commodity and social network and gives more satisfactory recommendation to users.

KW - Collaborative Filtering

KW - Item Attribute

KW - Recommendation Algorithm

KW - Similarity

KW - User Rating

UR - http://www.scopus.com/inward/record.url?scp=85037536970&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85037536970

T2 - 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017

Y2 - 2 November 2017 through 5 November 2017

ER -

Collaborative filtering recommendation algorithm based on user ratings and item attributes

Abstract

Conference

Keywords

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