Research on personalized recommendation in E-commerce service based on data mining

Tao Xu, Jing Tian, Tomohiro Murata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a new hybrid recommendation algorithm to optimization the cold-start problem with Collaborative Filtering (CF). And we use neighborhood-based collaborative filtering algorithm has obtained great favor due to simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. We introduce a new model comprising both In the training stage, user-item and film-item relationships in recommender systems, and describe how to use algorithm generates recommendations for cold-start items based on the preference model. Our experiments model provides a relatively efficient and accurate recommendation technique.

本文言語English
ホスト出版物のタイトルProceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
出版社Newswood Limited
ページ313-317
ページ数5
ISBN(印刷版)9789881925183
出版ステータスPublished - 2013 1 1
イベントInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013 - Kowloon, Hong Kong
継続期間: 2013 3 132013 3 15

出版物シリーズ

名前Lecture Notes in Engineering and Computer Science
2202
ISSN(印刷版)2078-0958

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2013, IMECS 2013
国/地域Hong Kong
CityKowloon
Period13/3/1313/3/15

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)

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