NDLI: A Cold-Start Recommendation Algorithm Based on New User's Implicit Information and Multi-attribute Rating Matrix

Content Provider	IEEE Xplore Digital Library
Author	Yin Hang Chang Guiran Wang Xingwei
Copyright Year	2009
Abstract	Traditional collaborative filtering recommendation algorithms face the cold-start problem. A collaborative filtering recommendation algorithm based on the implicit information of the new users and multi-attribute rating matrix is proposed to solve the problem. The implicit information of the new users is collected as the first-hand interest information. It is combined with other rating information to create a User-Item Rating Matrix (UIRM). Singular Value Decomposition is used to reduce the dimensionality of the UIRM, resulting in the initial neighbor set for target users and a new user-item rating matrix. The user ratings are mapped to the relevant item attributes and the user attributes respectively to generate a User-Item Attribute Rating Matrix and a User Attribute-Item Attribute Rating Matrix (UAIARM). The attributes of new items and UAIARM are matched to find the N users with the highest match degrees as the target of the new items. The attributes of the new users are matched with UAIARM to find the N items with the highest match degrees as the recommended items. Experiment results validate the feasibility of the algorithm.
Starting Page	353
Ending Page	358
File Size	627666
Page Count	6
File Format	PDF
ISBN	9780769537450
DOI	10.1109/HIS.2009.184
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	collaborative filtering attribute rating matrix implicit information International collaboration Information filtering Matrix decomposition cold-start Nearest neighbor searches Information science Filtering algorithms Information filters recommendation algorithm Internet Hybrid intelligent systems Singular value decomposition
Content Type	Text
Resource Type	Article

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